Dataset description

Daily official UK Covid data. The data is available per country (England, Scotland, Wales and Northern Ireland) and for different regions in England. The different regions are split into two different files as part of the data is directly gathered by the NHS (National Health Service). The files that contain the word 'nhsregion' in their name, include data related to hospitals only, such as number of admissions or number of people in respirators. The files containing the word 'region' in their name, include the rest of the data, such as number of cases, number of vaccinated people or number of tests performed per day. The next paragraphs describe the columns for the different file types.

Region files

Files related to regions (word 'region' included in the file name) have the following columns: - "date": date in YYYY-MM-DD format - "area type": type of area covered in the file (region or nation) - "area name": name of area covered in the file (region or nation name) - "daily cases": new cases on a given date - "cum cases": cumulative cases - "new deaths 28days": new deaths within 28 days of a positive test - "cum deaths 28days": cumulative deaths within 28 days of a positive test - "new deaths_60days": new deaths within 60 days of a positive test - "cum deaths 60days": cumulative deaths within 60 days of a positive test - "new_first_episode": new first episodes by date - "cum_first_episode": cumulative first episodes by date - "new_reinfections": new reinfections by specimen data - "cum_reinfections": cumualtive reinfections by specimen data - "new_virus_test": new virus tests by date - "cum_virus_test": cumulative virus tests by date - "new_pcr_test": new PCR tests by date - "cum_pcr_test": cumulative PCR tests by date - "new_lfd_test": new LFD tests by date - "cum_lfd_test": cumulative LFD tests by date - "test_roll_pos_pct": percentage of unique case positivity by date rolling sum - "test_roll_people": unique people tested by date rolling sum - "new first dose": new people vaccinated with a first dose - "cum first dose": cumulative people vaccinated with a first dose - "new second dose": new people vaccinated with a first dose - "cum second dose": cumulative people vaccinated with a first dose - "new third dose": new people vaccinated with a booster or third dose - "cum third dose": cumulative people vaccinated with a booster or third dose

Country files

Files related to countries (England, Northern Ireland, Scotland and Wales) have the above columns and also: - "new admissions": new admissions, - "cum admissions": cumulative admissions, - "hospital cases": patients in hospitals, - "ventilator beds": COVID occupied mechanical ventilator beds - "trans_rate_min": minimum transmission rate (R) - "trans_rate_max": maximum transmission rate (R) - "trans_growth_min": transmission rate growth min - "trans_growth_max": transmission rate growth max

NHS Region files

Files related to nhsregion (word 'nhsregion' included in the file name) have the following columns: - "new admissions": new admissions, - "cum admissions": cumulative admissions, - "hospital cases": patients in hospitals, - "ventilator beds": COVID occupied mechanical ventilator beds - "trans_rate_min": minimum transmission rate (R) - "trans_rate_max": maximum transmission rate (R) - "trans_growth_min": transmission rate growth min - "trans_growth_max": transmission rate growth max

It's worth noting that the dataset hasn't been cleaned and it needs cleaning. Also, different files have different null columns. This isn't an error in the dataset but the way different countries and regions report the data.

The COVID-19 dashboard includes data on city/town COVID-19 activity, confirmed and probable cases of COVID-19, confirmed and probable deaths related to COVID-19, and the demographic characteristics of cases and deaths.

http://dx.doi.org/10.1093/pubmed/fdab017On March 11, the WHO declared that the novel SARS-CoV-2 virus was at pandemic levels. In the UK, a number of public health measures such as social distancing and lockdown were introduced to minimise viral transmission. We sought to assess whether or not the initial outbreak of COVID-19 was associated with a change in the prescription rates of ICS, prednisolone, and antibiotics in primary care in England.Find the datasets and R scripts used for each medication analysis separately to confirm our results.

The Health Survey for England (HSE) is part of a programme of surveys commissioned by the Health and Social Care Information Centre. It has been carried out since 1994 by the Joint Health Surveys Unit of NatCen Social Research and the Research Department of Epidemiology and Public Health at UCL (University College London). The study provides regular information that cannot be obtained from other sources on a range of aspects concerning the public's health and many of the factors that affect health. The series of Health Surveys for England was designed to monitor trends in the nation's health, to estimate the proportion of people in England who have specified health conditions, and to estimate the prevalence of certain risk factors and combinations of risk factors associated with these conditions. The survey is also used to monitor progress towards selected health targets. Each survey in the series includes core questions and measurements (such as blood pressure, anthropometric measurements and analysis of blood and saliva samples), as well as modules of questions on specific issues that vary from year to year. In some years, the core sample has also been augmented by an additional boosted sample from a specific population subgroup, such as minority ethnic groups, older people or children; there was no boost in 2011. This is the twenty first annual Health Survey for England. All surveys have covered the adult population aged 16 and over living in private households in England. Since 1995, the surveys have included children who live in households selected for the survey; children aged 2-15 were included from 1995, and infants under two years old were added in 2001. Those living in institutions were outside the scope of the survey. This should be borne in mind when considering survey findings, since the institutional population is likely to be older and less healthy than those living in private households. The HSE in 2011 provided a representative sample of the population at both national and regional level. For the general population sample, 8,992 addresses were randomly selected in 562 postcode sectors, issued over twelve months from January to December 2011. Where an address was found to have multiple dwelling units, a random selection was made and a single dwelling unit was included. Where there were multiple households at a dwelling unit, again one was selected at random. All adults and children in selected households were eligible for inclusion in the survey. Where there were three or more children aged 0-15 in a household, two of the children were selected at random to limit the respondent burden for parents. A nurse visit was arranged for all participants who consented. A total of 8,610 adults and 2,007 children were interviewed. A household response rate of 66per cent was achieved. 5,715 adults and 1,257 children had a nurse visit. It should be noted that, for the first time for several years, there was no child boost sample in 2011. Thus the scope for analyses of some data for children may be limited by relatively small sample sizes. The report authors would like to acknowledge with thanks the contribution of the National Obesity Observatory to Chapter 10 on adult obesity.

IntroductionAntibiotic resistance poses a threat to public health and healthcare systems. Escherichia coli causes more bacteraemia episodes in England than any other bacterial species. This study aimed to estimate the burden of E. coli bacteraemia and associated antibiotic resistance in the secondary care setting.Materials and methodsThis was a retrospective cohort study, with E. coli bacteraemia as the main exposure of interest. Adult hospital in-patients, admitted to acute NHS hospitals between July 2011 and June 2012 were included. English national surveillance and administrative datasets were utilised. Cox proportional hazard, subdistribution hazard and multistate models were constructed to estimate rate of discharge, rate of in-hospital death and excess length of stay, with a unit bed day cost applied to the latter to estimate cost burden from the healthcare system perspective.Results14,042 E. coli bacteraemia and 8,919,284 non-infected inpatient observations were included. E. coli bacteraemia was associated with an increased rate of in-hospital death across all models, with an adjusted subdistribution hazard ratio of 5.88 (95% CI: 5.62–6.15). Resistance was not found to be associated with in-hospital mortality once adjusting for patient and hospital covariates. However, resistance was found to be associated with an increased excess length of stay. This was especially true for third generation cephalosporin (1.58 days excess length of stay, 95% CI: 0.84–2.31) and piperacillin/tazobactam resistance (1.23 days (95% CI: 0.50–1.95)). The annual cost of E. coli bacteraemia was estimated to be £14,346,400 (2012 £), with third-generation cephalosporin resistance associated with excess costs per infection of £420 (95% CI: 220–630).ConclusionsE. coli bacteraemia places a statistically significant burden on patient health and the hospital sector in England. Resistance to front-line antibiotics increases length of stay; increasing the cost burden of such infections in the secondary care setting.

The percentage of adults (aged 16 and over) that responded to the question "How often do you feel lonely?" with "Always or often" or "Some of the time"

Rationale At the beginning of 2018, the Prime Minister highlighted the issue of loneliness, announcing a Minister for Loneliness and committing to develop a national strategy to help tackle loneliness and a national measure for loneliness.

The national strategy, A Connected Society: A Strategy for Tackling Loneliness, was published on 15 October 2018. The commitments made by the Department of Health and Social Care (DHSC) and NHS England in the strategy identify loneliness to be a serious public health concern.

In keeping with the Loneliness Strategy, loneliness is defined here as: “a subjective, unwelcome feeling of lack or loss of companionship. It happens when we have a mismatch between the quantity and quality of social relationships that we have, and those that we want.” This is based on a definition first suggested by Perlman and Peplau in 1981(1).

Loneliness is a feeling that most people will experience at some point in their lives. When people feel lonely most or all of the time, it can have a serious impact on an individual’s well-being and their ability to function in society. Feeling lonely frequently is linked to early deaths and its health impact is thought to be on a par with other public health priorities like obesity or smoking.

Lonely people are more likely to be readmitted to hospital or have a longer stay. There is also evidence that lonely people are more likely to visit a General Practitioner or Accident and Emergency and more likely to enter local authority funded residential care.

At work, higher loneliness among employees is associated with poorer performance on tasks and in a team, while social interaction at work has been linked to increased productivity.

Loneliness can affect anyone of any age and background. It is important to measure loneliness because the evidence on loneliness is currently much more robust and extensive on loneliness in older people, but much less for other age groups including children and young people.

If more people measure loneliness in the same way, we will build a much better evidence base more quickly. That’s why the Prime Minister asked the Office for National Statistics (ONS) to develop national indicators of loneliness for people of all ages, suitable for use on major studies.

When reporting the prevalence of loneliness, ONS advise using the responses from the direct question, “How often do you feel lonely?” The inclusion of the direct loneliness measure in the Public Health Outcomes Framework (PHOF) will help inform and focus future work on loneliness at both a national and local level, providing a focus to support strategic leadership, policy decisions and service commissioning.

In this first set of data on loneliness prevalence at a local authority level, we have merged the two most frequent categories of feeling lonely (often or always and some of the time). This is due to small sample sizes and the limitations of this data will be explained in more detail in the caveats section.

This will be replaced next year by a 2-year pooled dataset which will have large enough sample sizes to report chronic loneliness. Presenting the data this year will help local authorities to work preventatively to tackle chronic loneliness by showing whether a local area has higher than national average levels of loneliness.

(1) Perlman D and Peplau LA (1981) 'Toward a Social Psychology of Loneliness', in Gilmour R and Duck S (eds.), Personal Relationships. 3, Personal Relationships in Disorder, London: Academic Press, pp. 31–56.

Definition of numerator Weighted number of respondents aged 16 and over, with a valid response to the question "How often do you feel lonely" that answered "Always or often" or "Some of the time". Active Lives Adult Survey data is collected November to November.

Definition of denominator Weighted number of respondents aged 16 and over, with a valid response to the question "How often do you feel lonely?".Denominator values in the Download data are unweighted counts. All analyses for this indicator have been weighted to be representative of the population of England.Active Lives Adult Survey data is collected November to November.

Caveats

Due to the sample size at local authority level, the "often or always" category is merged with the next most severe category of loneliness (people who respond as feeling lonely “some of the time”).

Standard practice is to report the two categories separately. However, data from other sources shows a degree of volatility in the ratio between these categories at the local authority (LA) level.

Therefore, there is a risk that when two local authorities are both reported as having 25% of people feeling lonely (often or always combined with some of the time), the actual figures for "often or always" might differ significantly. For example, one LA might have 24% often and always while another has only 3%, which would not be apparent in the combined category.

This could lead to underestimation or overestimation of chronic loneliness levels by local authorities.

Number of deaths registered each month by area of usual residence for England and Wales, by region, county, health authorities, local and unitary authority, and London borough.

Risk perception sub-scales with final factor solutions.

August 2024 marked a significant shift in the UK's monetary policy, as it saw the first reduction in the official bank base interest rate since August 2023. This change came after a period of consistent rate hikes that began in late 2021. In a bid to minimize the economic effects of the COVID-19 pandemic, the Bank of England cut the official bank base rate in March 2020 to a record low of *** percent. This historic low came just one week after the Bank of England cut rates from **** percent to **** percent in a bid to prevent mass job cuts in the United Kingdom. It remained at *** percent until December 2021 and was increased to one percent in May 2022 and to **** percent in October 2022. After that, the bank rate increased almost on a monthly basis, reaching **** percent in August 2023. It wasn't until August 2024 that the first rate decrease since the previous year occurred, signaling a potential shift in monetary policy. Why do central banks adjust interest rates? Central banks, including the Bank of England, adjust interest rates to manage economic stability and control inflation. Their strategies involve a delicate balance between two main approaches. When central banks raise interest rates, their goal is to cool down an overheated economy. Higher rates curb excessive spending and borrowing, which helps to prevent runaway inflation. This approach is typically used when the economy is growing too quickly or when inflation is rising above desired levels. Conversely, when central banks lower interest rates, they aim to encourage borrowing and investment. This strategy is employed to stimulate economic growth during periods of slowdown or recession. Lower rates make it cheaper for businesses and individuals to borrow money, which can lead to increased spending and investment. This dual approach allows central banks to maintain a balance between promoting growth and controlling inflation, ensuring long-term economic stability. Additionally, adjusting interest rates can influence currency values, impacting international trade and investment flows, further underscoring their critical role in a nation's economic health. Recent interest rate trends Between 2021 and 2025, most advanced and emerging economies experienced a period of regular interest rate hikes. This trend was driven by several factors, including persistent supply chain disruptions, high energy prices, and robust demand pressures. These elements combined to create significant inflationary trends, prompting central banks to raise rates to temper spending and borrowing. However, in 2024, a shift began to occur in global monetary policy. The European Central Bank (ECB) was among the first major central banks to reverse this trend by cutting interest rates. This move signaled a change in approach aimed at addressing growing economic slowdowns and supporting growth.

This is the second (wave 2) in a series of follow up reports to the Mental Health and Young People Survey (MHCYP) 2017, exploring the mental health of children and young people in February/March 2021, during the Coronavirus (COVID-19) pandemic and changes since 2017. Experiences of family life, education, and services during the COVID-19 pandemic are also examined. The sample for the Mental Health Survey for Children and Young People, 2021 (MHCYP 2021), wave 2 follow up was based on 3,667 children and young people who took part in the MHCYP 2017 survey, with both surveys also drawing on information collected from parents. Cross-sectional analyses are presented, addressing three primary aims: Aim 1: Comparing mental health between 2017 and 2021 – the likelihood of a mental disorder has been assessed against completion of the Strengths and Difficulties Questionnaire (SDQ) in both years in Topic 1 by various demographics. Aim 2: Describing life during the COVID-19 pandemic - Topic 2 examines the circumstances and experiences of children and young people in February/March 2021 and the preceding months, covering: COVID-19 infection and symptoms. Feelings about social media use. Family connectedness. Family functioning. Education, including missed days of schooling, access to resources, and support for those with Special Educational Needs and Disabilities (SEND). Changes in circumstances. How lockdown and restrictions have affected children and young people’s lives. Seeking help for mental health concerns. Aim 3: Present more detailed data on the mental health, circumstances and experiences of children and young people by ethnic group during the coronavirus pandemic (where sample sizes allow). The data is broken down by gender and age bands of 6 to 10 year olds and 11 to 16 year olds for all categories, and 17 to 22 years old for certain categories where a time series is available, as well as by whether a child is unlikely to have a mental health disorder, possibly has a mental health disorder and probably has a mental health disorder. This study was funded by the Department of Health and Social Care, commissioned by NHS Digital, and carried out by the Office for National Statistics, the National Centre for Social Research, University of Cambridge and University of Exeter.

In 2024, the gross domestic product (GDP) of the United Kingdom grew by 0.9 percent and is expected to grow by just one percent in 2025 and by 1.9 percent in 2026. Growth is expected to slow down to 1.8 percent in 2027, and then grow by 1.7, and 1.8 percent in 2027 and 2028 respectively. The sudden emergence of COVID-19 in 2020 and subsequent closure of large parts of the economy were the cause of the huge 9.4 percent contraction in 2020, with the economy recovering somewhat in 2021, when the economy grew by 7.6 percent. UK growth downgraded in 2025 Although the economy is still expected to grow in 2025, the one percent growth anticipated in this forecast has been halved from two percent in October 2024. Increased geopolitical uncertainty as well as the impact of American tariffs on the global economy are some of the main reasons for this mark down. The UK's inflation rate for 2025 has also been revised, with an annual rate of 3.2 percent predicated, up from 2.6 percent in the last forecast. Unemployment is also anticipated to be higher than initially thought, with the annual unemployment rate likely to be 4.5 percent instead of 4.1 percent. Long-term growth problems In the last two quarters of 2023, the UK economy shrank by 0.1 percent in Q3 and by 0.3 percent in Q4, plunging the UK into recession for the first time since the COVID-19 pandemic. Even before that last recession, however, the UK economy has been struggling with weak growth. Although growth since the pandemic has been noticeably sluggish, there has been a clear long-term trend of declining growth rates. The economy has consistently been seen as one of the most important issues to people in Britain, ahead of health, immigration and the environment. Achieving strong levels of economic growth is one of the main aims of the Labour government elected in 2024, although after almost one year in power it has so far proven elusive.

Regression coefficients for compliant behaviours.

Objective Gains in life expectancy have faltered in several high-income countries in recent years. We aim to compare life expectancy trends in Scotland to those seen internationally, and to assess the timing of any recent changes in mortality trends for Scotland. Setting Austria, Croatia, Czech Republic, Denmark, England & Wales, Estonia, France, Germany, Hungary, Iceland, Israel, Japan, Korea, Latvia, Lithuania, Netherlands, Northern Ireland, Poland, Scotland, Slovakia, Spain, Sweden, Switzerland, USA. Methods We used life expectancy data from the Human Mortality Database (HMD) to calculate the mean annual life expectancy change for 24 high-income countries over five-year periods from 1992 to 2016, and the change for Scotland for five-year periods from 1857 to 2016. One- and two-break segmented regression models were applied to mortality data from National Records of Scotland (NRS) to identify turning points in age-standardised mortality trends between 1990 and 2018. Results In 2012-2016 life expectancies in Scotland increased by 2.5 weeks/year for females and 4.5 weeks/year for males, the smallest gains of any period since the early 1970s. The improvements in life expectancy in 2012-2016 were smallest among females (<2.0 weeks/year) in Northern Ireland, Iceland, England & Wales and the USA and among males (<5.0 weeks/year) in Iceland, USA, England & Wales and Scotland. Japan, Korea, and countries of Eastern Europe have seen substantial gains in the same period. The best estimate of when mortality rates changed to a slower rate of improvement in Scotland was the year to 2012 Q4 for males and the year to 2014 Q2 for females. Conclusion Life expectancy improvement has stalled across many, but not all, high income countries. The recent change in the mortality trend in Scotland occurred within the period 2012-2014. Further research is required to understand these trends, but governments must also take timely action on plausible contributors. Methods Description of methods used for collection/generation of data: The HMD has a detailed methods protocol available here: https://www.mortality.org/Public/Docs/MethodsProtocol.pdf The ONS and NRS also have similar methods for ensuring data consistency and quality assurance.

Methods for processing the data: The segmented regression was conducted using the 'segmented' package in R. The recommended references to this package and its approach are here: Vito M. R. Muggeo (2003). Estimating regression models with unknown break-points. Statistics in Medicine, 22, 3055-3071.

Vito M. R. Muggeo (2008). segmented: an R Package to Fit Regression Models with Broken-Line Relationships. R News, 8/1, 20-25. URL https://cran.r-project.org/doc/Rnews/.

Vito M. R. Muggeo (2016). Testing with a nuisance parameter present only under the alternative: a score-based approach with application to segmented modelling. J of Statistical Computation and Simulation, 86, 3059-3067.

Vito M. R. Muggeo (2017). Interval estimation for the breakpoint in segmented regression: a smoothed score-based approach. Australian & New Zealand Journal of Statistics, 59, 311-322.

Software- or Instrument-specific information needed to interpret the data, including software and hardware version numbers: The analyses were conducted in R version 3.6.1 and Microsoft Excel 2013.

Please see README.txt for further information

The COVID-19 pandemic has had a substantial impact on mental health; because students are particularly vulnerable to loneliness, isolation, stress and unhealthy lifestyle choices, their mental health and wellbeing may potentially be more severely impacted by lockdown measures than the general population. This study assessed the mental health and wellbeing of UK undergraduate students during and after the lockdowns associated with the COVID-19 pandemic. Data were collected via online questionnaire at 3 time points – during the latter part of the first wave of the pandemic (spring/summer 2020; n=46) while stringent lockdown measures were still in place but gradually being relaxed; during the second wave of the pandemic (winter 2020-21; n=86) while local lockdowns were in place across the UK; and during the winter of 2021-22 (n=77), when infection rates were high but no lockdown measures were in place. Stress was found to most strongly predict wellbeing and mental health measures during the two pandemic waves. Other substantial predictors were diet quality and intolerance of uncertainty. Positive wellbeing was the least well accounted for of our outcome variables. Conversely, we found that depression and anxiety were higher during winter 2021-22 (no lockdowns) than winter 2020-21 (under lockdown). This may be due to the high rates of infection over that period and the effects of COVID-19 infection itself on mental health. This suggests that, as significant as the effects of lockdowns were on the wellbeing of the nation, not implementing lockdown measures could potentially have been even more detrimental for mental health.

The SHMI is the ratio between the actual number of patients who die following hospitalisation at the trust and the number that would be expected to die on the basis of average England figures, given the characteristics of the patients treated there. It includes deaths which occurred in hospital and deaths which occurred outside of hospital within 30 days (inclusive) of discharge. The SHMI gives an indication for each non-specialist acute NHS trust in England whether the observed number of deaths within 30 days of discharge from hospital was 'higher than expected' (SHMI banding=1), 'as expected' (SHMI banding=2) or 'lower than expected' (SHMI banding=3) when compared to the national baseline. Trusts may be located at multiple sites and may be responsible for 1 or more hospitals. A breakdown of the data by site of treatment is also provided. The SHMI is composed of 144 different diagnosis groups and these are aggregated to calculate the overall SHMI value for each trust. The number of finished provider spells, observed deaths and expected deaths at diagnosis group level for each trust is available in the SHMI diagnosis group breakdown files. For a subset of diagnosis groups, an indication of whether the observed number of deaths within 30 days of discharge from hospital was 'higher than expected', 'as expected' or 'lower than expected' when compared to the national baseline is also provided. Details of the 144 diagnosis groups can be found in Appendix A of the SHMI specification. Notes: 1. There is a shortfall in the number of records for East Lancashire Hospitals NHS Trust (trust code RXR) and Harrogate and District NHS Foundation Trust (trust code RCD). Values for these trusts are based on incomplete data and should therefore be interpreted with caution. 2. Frimley Health NHS Foundation Trust (trust code RDU) stopped submitting data to the Secondary Uses Service (SUS) during June 2022 and did not start submitting data again until April 2023 due to an issue with their patient records system. This is causing a large shortfall in records and values for this trust should be viewed in the context of this issue. 3. Royal Surrey County Hospital NHS Foundation Trust (trust code RA2) has a high percentage of records with no data for secondary diagnoses. This is having a large impact on this trust’s data and values for this trust should therefore be interpreted with caution. 4. There is a high percentage of invalid diagnosis codes for Chesterfield Royal Hospital NHS Foundation Trust (trust code RFS), East Lancashire Hospitals NHS Trust (trust code RXR), Portsmouth Hospitals University NHS Trust (trust code RHU), and University Hospitals Plymouth NHS Trust (trust code RK9). Values for these trusts should therefore be interpreted with caution. 5. A number of trusts are now submitting Same Day Emergency Care (SDEC) data to the Emergency Care Data Set (ECDS) rather than the Admitted Patient Care (APC) dataset. The SHMI is calculated using APC data. Removal of SDEC activity from the APC data may impact a trust’s SHMI value and may increase it. More information about this is available in the SHMI background quality report. 6. Further information on data quality can be found in the SHMI background quality report, which can be downloaded from the 'Resources' section of this page.

Moderate and severe AEs by COVID-19 status: Percentage of cases reporting moderate or severe AEs following BNT162b2/Pfizer third/booster dose (95% CI) in those with and without a history of COVID-19 (the former including OSC).

Tea is quintessentially British – according to the UK Tea & Infusions Association, over 100 million cups are drunk in the UK every single day. Over the past five years, the Tea Processing industry has experienced swings in revenue. Exposed supply chains have left prices vulnerable, global conflicts and periods of reduced demand have caused prices to fall. Combined with the cost-of-living crisis, industry revenue and profit have been bumpy. Coffee has also soared in popularity, threatening tea's place as a core part of British culture. However, recent trends seem to be turning that around. Rising health consciousness has made tea trendy, aided by social media and wellness trends. Tea offers numerous health benefits due to the presence of antioxidants and less caffeine than coffee. Additionally, recent consumer tastes highlight a desire for functional benefits for food and drink – as a result, herbal and speciality teas in particular have benefitted. Tea processors have been quick to respond, diversifying product offerings as they are keen to soak up the emerging demand. In 2025, Pukka re-launched its herbal blends, with a focus on the health and wellness benefits they can bring. Smaller companies have also emerged, like PerfectTed, tapping into gaps in the market for emerging tea tastes, like matcha. However, the Bank of England is anticipating inflation to rise in 2025 and there is continued concern over shipping in the Red Sea due to ongoing conflict, overwhelming industry optimism. Tapping into these health trends will be key for companies to mitigate the negative shock. Over the five years through 2025-26, revenue is anticipated to fall at a compound annual rate of 2.8% to £1 billion. In 2025-26, revenue is expected to fall by 1.8%. During the five years through 2030-31, revenue is anticipated to rise at a compound annual rate of 0.8% to £1.1 billion. Although per-capita consumption of black tea will continue to fall as more consumers switch to other beverages, increasing emphasis on the health benefits of other types of tea and introducing new products (like fruit and herbal infusions and bubble tea) will likely benefit the industry. However, for long-term prosperity, tea processors can ensure the sustainability of their supply chain as climate change threatens tea yields.

In October 2025, 63 percent of households in Great Britain reported that their cost of living had increased in the previous month, compared with 72 percent in April. Although the share of people reporting a cost of living increase has generally been falling since August 2022, when 91 percent of households reported an increase, the most recent figures indicate that the Cost of Living Crisis is still ongoing for many households in the UK. Crisis ligers even as inflation falls Although various factors have been driving the Cost of Living Crisis in Britain, high inflation has undoubtedly been one of the main factors. After several years of relatively low inflation, the CPI inflation rate shot up from 2021 onwards, hitting a high of 11.1 percent in October 2022. In the months since that peak, inflation has fallen to more usual levels, and was 2.5 percent in December 2024, slightly up from 1.7 percent in September. Since June 2023, wages have also started to grow at a faster rate than inflation, albeit after a long period where average wages were falling relative to overall price increases. Economy continues to be the main issue for voters Ahead of the last UK general election, the economy was consistently selected as the main issue for voters for several months. Although the Conservative Party was seen by voters as the best party for handling the economy before October 2022, this perception collapsed following the market's reaction to Liz Truss' mini-budget. Even after changing their leader from Truss to Rishi Sunak, the Conservatives continued to fall in the polls, and would go onto lose the election decisively. Since the election, the economy remains the most important issue in the UK, although it was only slightly ahead of immigration and health as of January 2025.