This data shows premature deaths (Age under 75) from Liver Disease, numbers and rates by gender, as 3-year moving-averages. Most liver disease is preventable and much is influenced by alcohol consumption and obesity prevalence, which are both amenable to public health interventions. Directly Age-Standardised Rates (DASR) are shown in the data (where numbers are sufficient) so that death rates can be directly compared between areas. The DASR calculation applies Age-specific rates to a Standard (European) population to cancel out possible effects on crude rates due to different age structures among populations, thus enabling direct comparisons of rates. A limitation on using mortalities as a proxy for prevalence of health conditions is that mortalities may give an incomplete view of health conditions in an area, as ill-health might not lead to premature death. Low numbers may result in zero values or missing data. Data source: Office for Health Improvement and Disparities (OHID), Public Health Outcomes Framework (PHOF) indicator 40601 (E06a). The data is updated annually.

All data relating to Obesity and mortality during the coronavirus (COVID-19) pandemic, England: 24 January 2020 to 30 August 2022

Note: This dataset has been archived as of January 2024, as will not be made available to the public. Information that the data is ward-level aggregated which is volatile and easy to misinterpret the data and the level datasets are misleading if published. This data shows the percentage of adults (age 18 and over) classed as having Excess Weight. Excess Weight is a major cause of premature deaths and avoidable ill-health. Excess weight is a term used for overweight, which includes obesity. Excess weight is defined in adults as a BMI greater than or equal to 25kg/m2. The data is age-standardised, so differences in the population age structures of areas will not affect comparison of rates. This dataset shows estimates based on sample sizes that may be quite small particularly at district level, so not too much should not be read into apparent differences between districts which might not be statistically significant. Thus as with many other datasets, this data should be used together with other data and resources to obtain a fuller picture. Data source: Public Health England, Public Health Outcomes Framework (PHOF) indicator 2.12. This data is updated annually.

Obesity traits are causally implicated with risk of cardiometabolic diseases. It remains unclear whether there are similar causal effects of obesity traits on other non-communicable diseases. Also, it is largely unexplored whether there are any sex-specific differences in the causal effects of obesity traits on cardiometabolic diseases and other leading causes of death. We constructed sex-specific genetic risk scores (GRS) for three obesity traits; body mass index (BMI), waist-hip ratio (WHR), and WHR adjusted for BMI, including 565, 324, and 337 genetic variants, respectively. These GRSs were then used as instrumental variables to assess associations between the obesity traits and leading causes of mortality in the UK Biobank using Mendelian randomization. We also investigated associations with potential mediators, including smoking, glycemic and blood pressure traits. Sex-differences were subsequently assessed by Cochran’s Q-test (Phet). A Mendelian randomization analysis of 228,466 women and 195,041 men showed that obesity causes coronary artery disease, stroke (particularly ischemic), chronic obstructive pulmonary disease, lung cancer, type 2 and 1 diabetes mellitus, non-alcoholic fatty liver disease, chronic liver disease, and acute and chronic renal failure. Higher BMI led to higher risk of type 2 diabetes in women than in men (Phet = 1.4×10−5). Waist-hip-ratio led to a higher risk of chronic obstructive pulmonary disease (Phet = 3.7×10−6) and higher risk of chronic renal failure (Phet = 1.0×10−4) in men than women. Obesity traits have an etiological role in the majority of the leading global causes of death. Sex differences exist in the effects of obesity traits on risk of type 2 diabetes, chronic obstructive pulmonary disease, and renal failure, which may have downstream implications for public health.

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.

Bathing facilities and health phronesis: tackling English obesity. Mixed methods sequential research in five phases.
Research questions and hypotheses • RQ1: Does the geospatial distribution of swimming facilities impact health? (Nomothetic). (H10: Pools is insignificant vs. H1A: Pools is significant) • RQ2: Is the construction of swimming pools adequate for national health need? (Nomothetic). (H20: Forecast pool construction stable vs. H2A: Forecast pool construction increases) • RQ3: What policy learning emerges from idiosyncratic cases? (Idiographic & qualitative) Approach After problematisation (1) and structured literature review (2), the study conducted cross-sectional analysis of excess mortality and swimming pools (3a & 3b) and longitudinal analysis of pool construction (3c-e). Cross-sectional investigation involved factor analysis (3a) to explore and regression to analysis (3b) to investigate English mortality and its covariates (3b). The For the time series analysis, the study analysed 120 years of English pool construction data using autoregressive distributed lag models - ARIMA (3c), ADL (3d) and ECM (3e).
Data Cross sectional analysis Deaths (DV, Yd): ONS standardised mortality ratio (2013-2017). Observed total deaths from all causes (by five year age and gender band) as a percentage of expected deaths.
Access Leisure (IV, X1): reflects accessibility to 727 leisure centres, swimming baths or 2,738 health clubs in kilometres. Liverpool University’s Consumer Data Research Centre, Access to Healthy Assets and Hazards (AHAH) index. Obesity (IV, X2): percentage of adult population with a body mass index (BMI) of 30 kg/m2 or higher, age-standardized, WHO 2389 NCD_BMI_30 (2020). Deprivation (IV, X3): deprivation score for English small areas, sourced from Index of Multiple Deprivation (2019). Environment (IV, X4) measures accessible blue and green space, sourced via SE (2020), data constitutes an element of AHAH (2017).
Pools (IV, X5): reflects pools per 10,000 in 2018. Data extracted from SE Active Places Power (APP) Time series analysis Pools constructed (PC & ∆PC): English swimming pools constructed each year during a 120 year period since 1900, SE Active Places Power (2020) database. English output (GDP & ∆GDP): Bank of England millennium of macroeconomic data UK (2017) provides historical macroeconomic and financial statistics.
English population (Pop & ∆Pop): English population and population growth 1900-2020, Office for National Statistics (ONS): Total population (2018).

Background. Chronic obstructive pulmonary disease (COPD) is a debilitating lung condition characterised by progressive lung function limitation. COPD is an umbrella term and encompasses a spectrum of pathophysiologies including chronic bronchitis, small airways disease and emphysema. COPD caused an estimated 3 million deaths worldwide in 2016, and is estimated to be the third leading cause of death worldwide. The British Lung Foundation (BLF) estimates that the disease costs the NHS around £1.9 billion per year. COPD is therefore a significant public health challenge. This dataset explores the impact of hospitalisation in patients with COPD during the COVID pandemic.

PIONEER geography The West Midlands (WM) has a population of 5.9 million & includes a diverse ethnic & socio-economic mix. There is a higher than average percentage of minority ethnic groups. WM has a large number of elderly residents but is the youngest population in the UK. There are particularly high rates of physical inactivity, obesity, smoking & diabetes. The West Midlands has a high prevalence of COPD, reflecting the high rates of smoking and industrial exposure. Each day >100,000 people are treated in hospital, see their GP or are cared for by the NHS.

EHR. University Hospitals Birmingham NHS Foundation Trust (UHB) is one of the largest NHS Trusts in England, providing direct acute services & specialist care across four hospital sites, with 2.2 million patient episodes per year, 2750 beds & 100 ITU beds. UHB runs a fully electronic healthcare record (EHR) (PICS; Birmingham Systems), a shared primary & secondary care record (Your Care Connected) & a patient portal “My Health”.

Scope: All hospitalised patients admitted to UHB during the COVID-19 pandemic first wave, curated to focus on COPD. Longitudinal & individually linked, so that the preceding & subsequent health journey can be mapped & healthcare utilisation prior to & after admission understood. The dataset includes ICD-10 & SNOMED-CT codes pertaining to COPD and COPD exacerbations, as well as all co-morbid conditions. Serial, structured data pertaining to process of care (timings, staff grades, specialty review, wards), presenting complaint, all physiology readings (pulse, blood pressure, respiratory rate, oxygen saturations), all blood results, microbiology, all prescribed & administered treatments (fluids, nebulisers, antibiotics, inotropes, vasopressors, organ support), all outcomes. Linked images available (radiographs, CT).

Available supplementary data: More extensive data including wave 2 patients in non-OMOP form. Ambulance, 111, 999 data, synthetic data.

Available supplementary support: Analytics, Model build, validation & refinement; A.I.; Data partner support for ETL (extract, transform & load) process, Clinical expertise, Patient & end-user access, Purchaser access, Regulatory requirements, Data-driven trials, “fast screen” services.

Deeply phenotyped sepsis patients within hospital: onset, treatments & outcomes

Sepsis is life-threatening organ dysfunction due to a dysregulated host response to infection & is a global health challenge. In 2017, 48•9 million incident cases of sepsis were recorded worldwide with 11million sepsis-related deaths, representing 19•7% of all global deaths. There are >123,000 sepsis cases diagnosed in Engl& each year with an estimated 36,800 sepsis-associated deaths. Sepsis is treatable, & timely, targeted interventions improve outcomes. The World Health Assembly identified sepsis as a global health priority.

PIONEER geography The West Midlands (WM) has a population of 5.9 million & includes a diverse ethnic & socio-economic mix. There is a higher than average percentage of minority ethnic groups. WM has a large number of elderly residents but is the youngest population in the UK. Birmingham has the highest birth rate in England. It also has the highest infant mortality rate. WM life expectancy is 1.8 years less than in London. There are particularly high rates of physical inactivity, obesity, smoking & diabetes. Each day >100,000 people are treated in hospital, see their GP or are cared for by the NHS.

EHR. University Hospitals Birmingham NHS Foundation Trust (UHB) is one of the largest NHS Trusts in England, providing direct acute services & specialist care across four hospital sites, with 2.2 million patient episodes per year, 2750 beds & 100 ITU beds. UHB runs a fully electronic healthcare record (EHR) (PICS; Birmingham Systems), a shared primary & secondary care record (Your Care Connected) & a patient portal “My Health”.

Scope: All hospitalised patients to UHB from 2000 – current day. Updated monthly. Longitudinal & individually linked, so that the preceding & subsequent health journey can be mapped & healthcare utilisation prior to & after sepsis understood. The dataset includes ICD-10 & SNOMED-CT codes pertaining to sepsis & suspected sepsis. Serial, structured data pertaining to process of care (timings, staff grades, specialty review, wards), presenting complaint, all physiology readings (pulse, blood pressure, respiratory rate, oxygen saturations), all blood results, microbiology, all prescribed & administered treatments (fluids, antibiotics, inotropes, vasopressors, organ support), all outcomes. Linked images available (radiographs, CT, MRI, ultrasound). Includes COVID-19 wave 1 and wave 2 data.

Available supplementary data: Matched “non-sepsis” controls; ambulance, 111, 999 data, synthetic data.

Available supplementary support: Analytics, Model build, validation & refinement; A.I.; Data partner support for ETL (extract, transform & load) process, Clinical expertise, Patient & end-user access, Purchaser access, Regulatory requirements, Data-driven trials, “fast screen” services.