Number of excess deaths, including deaths due to coronavirus (COVID-19) and due to other causes. Including breakdowns by age, sex and geography.

Deaths registered in England and Wales in 2020 and how they compared with the five-year average (2015 to 2019), based on finalised 2020 mortality data. The figures are broken down by cause, place of death, age group, sex and deprivation.

Deaths registered in England and Wales by week, from 28 December 2019 to 2 July 2021. Breakdowns include country, sex, age group, region, place of death, and leading cause. Includes analysis of excess deaths and relative cumulative age-standardised mortality rates.

The percentage of extra deaths that occurred due to winter, including those that had COVID-19 mentioned on the death certificate. The Excess Winter Mortality (EWM) index is calculated as the number of excess winter deaths divided by the average non-winter deaths, expressed as a percentage. Calculated so that comparisons can be made between sexes, age groups, and regions.

An EWM index of 20 shows that there were 20 percent more deaths in winter compared with the non-winter period. Provisional figures at country and region level are produced for the most recent winter using estimation methods, and so are rounded to the nearest 100 deaths. Data post 2019/20 should be treated with caution due to high numbers of deaths from COVID-19 in the summer period.

For data years 2020/21 onwards, instances where the number of winter deaths compared to non-winter deaths were equal to zero or a negative value, an EWM index is presented. (For earlier years, the EWM index was removed). A zero value for winter deaths compared to non-winter deaths is often affected by rounding, so in these instances, the winter mortality index can either be a positive or negative value. A negative winter mortality index means there were a higher number of deaths in the non-winter periods than the winter period.

Alternatively, figures are available for deaths excluding COVID-19, calculated using all-cause deaths that did not have COVID-19 mentioned on the death certificate.

Data is Powered by LG Inform Plus and automatically checked for new data on the 3rd of each month.

Provisional counts of the number of total deaths and deaths not involving the coronavirus (COVID-19), between 28 December 2019 and 10 July 2020. This includes deaths disaggregated by age and sex; by region of England, and Wales, and place of death; and for underlying causes of death and deaths involving leading causes.

Novel coronavirus (COVID-19) is a new strain of coronavirus first identified in Wuhan, China. Clinical presentation may range from mild-to-moderate illness to pneumonia or severe acute respiratory infection. The COVID-19 pandemic has wider impacts on individuals' health, and their use of healthcare services, than those that occur as the direct result of infection. Reasons for this may include: * Individuals being reluctant to use health services because they do not want to burden the NHS or are anxious about the risk of infection. * The health service delaying preventative and non-urgent care such as some screening services and planned surgery. * Other indirect effects of interventions to control COVID-19, such as mental or physical consequences of distancing measures. This dataset provides information on trend data regarding the wider impact of the pandemic on the number of deaths in Scotland, derived from the National Records of Scotland (NRS) weekly deaths registration data. Data show recent trends in deaths (2020), whether COVID or non-COVID related, and historic trends for comparison (five-year average, 2015-2019). The recent trend data are shown by age group and sex, and the national data are also shown by broad area deprivation category (Scottish Index of Multiple Deprivation, SIMD). This data is also available on the COVID-19 Wider Impact Dashboard. Additional data sources relating to this topic area are provided in the Links section of the Metadata below. Information on COVID-19, including stay at home advice for people who are self-isolating and their households, can be found on NHS Inform. All publications and supporting material to this topic area can be found in the weekly COVID-19 Statistical Report. The date of the next release can be found on our list of forthcoming publications.

Provisional counts of the number of deaths registered in England and Wales, by age, sex, region and Index of Multiple Deprivation (IMD), in the latest weeks for which data are available.

Background and aimWith the Coronavirus Disease 2019 (COVID-19) pandemic continuing to impact healthcare systems around the world, healthcare providers are attempting to balance resources devoted to COVID-19 patients while minimizing excess mortality overall (both COVID-19 and non-COVID-19 patients). To this end, we conducted a systematic review (SR) to describe the effect of the COVID-19 pandemic on all-cause excess mortality (COVID-19 and non-COVID-19) during the pandemic timeframe compared to non-pandemic times.MethodsWe searched EMBASE, Cochrane Database of SRs, MEDLINE, Cumulative Index to Nursing and Allied Health Literature (CINAHL) and Cochrane Controlled Trials Register (CENTRAL), from inception (1948) to December 31, 2020. We used a two-stage review process to screen/extract data. We assessed risk of bias using Newcastle-Ottawa Scale (NOS). We used Critical Appraisal and Grading of Recommendations Assessment, Development and Evaluation (GRADE) methodology.ResultsOf 11,581 citations, 194 studies met eligibility. Of these studies, 31 had mortality comparisons (n = 433,196,345 participants). Compared to pre-pandemic times, during the COVID-19 pandemic, our meta-analysis demonstrated that COVID-19 mortality had an increased risk difference (RD) of 0.06% (95% CI: 0.06–0.06% p < 0.00001). All-cause mortality also increased [relative risk (RR): 1.53, 95% confidence interval (CI): 1.38–1.70, p < 0.00001] alongside non-COVID-19 mortality (RR: 1.18, 1.07–1.30, p < 0.00001). There was “very low” certainty of evidence through GRADE assessment for all outcomes studied, demonstrating the evidence as uncertain.InterpretationThe COVID-19 pandemic may have caused significant increases in all-cause excess mortality, greater than those accounted for by increases due to COVID-19 mortality alone, although the evidence is uncertain.Systematic review registration[https://www.crd.york.ac.uk/prospero/#recordDetails], identifier [CRD42020201256].

Background and aimWith the Coronavirus Disease 2019 (COVID-19) pandemic continuing to impact healthcare systems around the world, healthcare providers are attempting to balance resources devoted to COVID-19 patients while minimizing excess mortality overall (both COVID-19 and non-COVID-19 patients). To this end, we conducted a systematic review (SR) to describe the effect of the COVID-19 pandemic on all-cause excess mortality (COVID-19 and non-COVID-19) during the pandemic timeframe compared to non-pandemic times.MethodsWe searched EMBASE, Cochrane Database of SRs, MEDLINE, Cumulative Index to Nursing and Allied Health Literature (CINAHL) and Cochrane Controlled Trials Register (CENTRAL), from inception (1948) to December 31, 2020. We used a two-stage review process to screen/extract data. We assessed risk of bias using Newcastle-Ottawa Scale (NOS). We used Critical Appraisal and Grading of Recommendations Assessment, Development and Evaluation (GRADE) methodology.ResultsOf 11,581 citations, 194 studies met eligibility. Of these studies, 31 had mortality comparisons (n = 433,196,345 participants). Compared to pre-pandemic times, during the COVID-19 pandemic, our meta-analysis demonstrated that COVID-19 mortality had an increased risk difference (RD) of 0.06% (95% CI: 0.06–0.06% p < 0.00001). All-cause mortality also increased [relative risk (RR): 1.53, 95% confidence interval (CI): 1.38–1.70, p < 0.00001] alongside non-COVID-19 mortality (RR: 1.18, 1.07–1.30, p < 0.00001). There was “very low” certainty of evidence through GRADE assessment for all outcomes studied, demonstrating the evidence as uncertain.InterpretationThe COVID-19 pandemic may have caused significant increases in all-cause excess mortality, greater than those accounted for by increases due to COVID-19 mortality alone, although the evidence is uncertain.Systematic review registration[https://www.crd.york.ac.uk/prospero/#recordDetails], identifier [CRD42020201256].

Young Lives research has expanded to explore linking geographical data collected during the rounds to external datasets. Matching Young Lives data with administrative and geographic datasets significantly increases the scope for research in several areas, and may allow researchers to identify sources of exogenous variation for more convincing causal analysis on policy and/or early life circumstances.

Young Lives: Data Matching Series, 1900-2021 includes the following linked datasets:

1. Climate Matched Datasets (four YL study countries): Community-level GPS data has been matched with temperature and precipitation data from the University of Delaware. Climate variables are offered at the community level, with a panel data structure spanning across years and months. Hence, each community has a unique value of precipitation (variable PRCP) and temperature (variable TEMP), for each year and month pairing for the period 1900-2017.

2. COVID-19 Matched Dataset (Peru only): The YL Phone Survey Calls data has been matched with external data sources (The Peruvian Ministry of Health and the National Information System of Deaths in Peru). The matched dataset includes the total number of COVID cases per 1,000 inhabitants, the total number of COVID deaths by district and per 1,000 inhabitants; the total number of excess deaths per 1,000 inhabitants and the number of lockdown days in each Young Lives district in Peru during August 2020 to December 2021.

Further information is available in the PDF reports included in the study documentation.

Annual data on deaths registered by age, sex and selected underlying cause of death. Tables also provide both mortality rates and numbers of deaths over time.

Bathing facilities and health phronesis: a preliminary English investigation. 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 increase in pool construction) • 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).

Notable findings The evidence from cross sectional regression analysis (3b) supports the alternative hypothesis, H1A, that pool density significantly influences excess mortality in England. All three times series models project an increase in pool construction which lends support to H2A of an increased pool construction need. For RQ2 then, current levels of swimming pool construction appears inadequate.

Annual figures of winter mortality in England and Wales by sex, age group, cause, region, place of death and lower geographical areas.

Background and aimWith the Coronavirus Disease 2019 (COVID-19) pandemic continuing to impact healthcare systems around the world, healthcare providers are attempting to balance resources devoted to COVID-19 patients while minimizing excess mortality overall (both COVID-19 and non-COVID-19 patients). To this end, we conducted a systematic review (SR) to describe the effect of the COVID-19 pandemic on all-cause excess mortality (COVID-19 and non-COVID-19) during the pandemic timeframe compared to non-pandemic times.MethodsWe searched EMBASE, Cochrane Database of SRs, MEDLINE, Cumulative Index to Nursing and Allied Health Literature (CINAHL) and Cochrane Controlled Trials Register (CENTRAL), from inception (1948) to December 31, 2020. We used a two-stage review process to screen/extract data. We assessed risk of bias using Newcastle-Ottawa Scale (NOS). We used Critical Appraisal and Grading of Recommendations Assessment, Development and Evaluation (GRADE) methodology.ResultsOf 11,581 citations, 194 studies met eligibility. Of these studies, 31 had mortality comparisons (n = 433,196,345 participants). Compared to pre-pandemic times, during the COVID-19 pandemic, our meta-analysis demonstrated that COVID-19 mortality had an increased risk difference (RD) of 0.06% (95% CI: 0.06–0.06% p < 0.00001). All-cause mortality also increased [relative risk (RR): 1.53, 95% confidence interval (CI): 1.38–1.70, p < 0.00001] alongside non-COVID-19 mortality (RR: 1.18, 1.07–1.30, p < 0.00001). There was “very low” certainty of evidence through GRADE assessment for all outcomes studied, demonstrating the evidence as uncertain.InterpretationThe COVID-19 pandemic may have caused significant increases in all-cause excess mortality, greater than those accounted for by increases due to COVID-19 mortality alone, although the evidence is uncertain.Systematic review registration[https://www.crd.york.ac.uk/prospero/#recordDetails], identifier [CRD42020201256].

This indicator was put on hold in November 2016 until the introduction of the new mental health services data set (MHSDS) meant that a new indicator methodology could be developed. The indicator was republished with a new methodology in December 2020 and consequently comparisons should not be made to data published in 2016 and prior to this. In 2021 the methodology for this indicator was revised again, details of which can be found in the methodological change document within the resource links below. As such, comparisons between data using different methodologies should not be made. This indicator is a measure of the extent to which adults with a serious mental illness (SMI) die younger than adults without a serious mental illness (nSMI). To measure premature mortality in adults diagnosed with a serious mental illness (SMI). _ UPDATE February 2021: Two issues affecting the contextual information for indicator 1.5.i have been identified. Neither of these issues affected the indicator values and both have been corrected in the excel and CSV files for this indicator: Issue 1: The confidence intervals for the mental health mortality rate were originally calculated using Dobson’s method for counts where less than 389 deaths were observed. Although this is a valid method, the assured methodology for this indicator does not include this adjustment. The indicator specification has also been updated to remove reference to Dobson's method. Issue 2: There were some minor errors in the England level mental health population due to the inclusion of some duplicates.