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About this reportÌý

This reportÌýexplainsÌýthe quality andÌýmethodologyÌýinformation (QMI) for the ‘Cold mortality monitoring reports’ÌýofficialÌýstatistics published by theÌýUK Health Security Agency (UKHSA).

This QMI reportÌýhelps users understandÌýthe strengths and limitations of these statistics, ensuring UKHSA is compliant with the quality standardsÌýstatedÌýin theÌý.ÌýThe reportÌýexplains:Ìý

• the strengths and limitations of the data used to produce the statisticsÌý

• the methods used to produce the statisticsÌý

• the quality of the statistical outputs

About the statisticsÌý

Cold weather can cause people to become unwell through hypothermia, and falls and injuries from snow and ice. Cold temperatures also place stress on the body, increasing the risk of heart attack, stroke, lung problems and other diseases, and contribute to the spread of infectious diseases such as influenza. This can lead to increases in deaths, both during a period of cold weather and in the days or weeks afterwards.ÌýDuringÌýtheÌýwinter, UKHSA and the Met Office work together to issue Cold-Health AlertsÌý(CHAs)Ìýif the weather isÌýcold enoughÌýthat it has the potential to affect people’s health.Ìý

The annual ‘Cold mortality monitoring report’ provides information on deathsÌýduring cold episodes each year to inform public health actions. The statistics showÌýthe number of cold-associated deaths, both for the total impact of cold and for the direct impact of cold independently of impacts on influenza circulation. The total cold-associated deaths are also broken down by age, sex, region,Ìýcause of death and place of death. The report also discusses the timing of cold-associated deaths, and compares the cold-mortality relationship in the latest 5 years with a previous 5-year period.

The ‘Cold mortality monitoring report’ is being published as a new official statistic in development in 2026,ÌýdemonstratingÌýthat it is produced in line with the standards of trustworthiness,ÌýqualityÌýand value in theÌýCode of Practice for Statistics. UKHSA also produces annual heat mortality monitoring reports, which are currently available for summers from 2016 to 2024.

Geographical coverage:ÌýEngland

Publication frequency:ÌýAnnual

Changes to this document

18 February 2026: QMI report first published.

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Lead analyst:ÌýMo DaviesÌý

Contact information:Ìýextremeevents@ukhsa.gov.uk

Suitable data sourcesÌý

Statistics should be based on the mostÌýappropriate dataÌýto meet intended uses.Ìý

This section describes the data used to produce the statistics.Ìý

Data sourcesÌý

Data on deaths is derived from the Office for National Statistics (ONS) death registrations data. Final annual registrations data is used for deaths registered up to 2024, and provisional weekly registrations data is used for deaths registered from 2025 onwards. For each winter, data is extracted for deaths which occurred from October through to April of the following year. This is to allow analysis of delayed impacts of temperature on mortality throughout the winter, which is defined as November through to March of the following year.

Daily mean temperature data from the Met OfficeÌýat regional levelÌýis used in the statistical model. This is obtained through the UKHSA Environmental Public Health Surveillance System. An overall daily mean temperature for England is then obtained as a population-weighted average of the regional daily mean temperatures. Cold episodes are defined as a period of 2 or more consecutive days where the England overall daily mean temperature was 2°C or lower. This is consistent with the decision-making aid threshold used for issuing Yellow Cold Health Alerts.

Data on influenza activity is produced by the UKHSA Vaccines Analysis team. It is derived by multiplying weekly Royal College of General Practitioners (RCGP) influenza-like illness consultation rates by weekly influenza swab positivity rate using RCGP swabbing data. This provides an estimate at weekly level of the level of flu circulation in the England population overall.

°Õ³ó±ðÌý are used to calculate rates per million population.

Data qualityÌý

The data that we use to produce statistics must be fit for purpose.ÌýPoor qualityÌýdata can cause errors and can hinder effective decision making.

We have assessed the quality of the source data against the data quality dimensions in theÌýGovernment Data Quality Framework.

This assessment covers the quality of the data that was used to produce the statistics, not the quality of the final statistical outputs. °Õ³ó±ðÌýquality summary section belowÌýexplainsÌýthe quality of the final statistical outputs.

Strengths and limitations of theÌýdeathsÌýdataÌý

The strengths of the data include:Ìý

• a comprehensive record of deaths occurring in EnglandÌýbecause the registration of deaths is mandatory

• reviewÌýof deaths data carried out by ONS on a provisional basis throughout the year,Ìýas well as on a final basis annually

• a high levelÌýof completion ofÌýall required information for analysisÌýbecause death certificates haveÌýrequiredÌýfields

• usingÌýinternational standardsÌýto code underlying causes of death

The mainÌýlimitation of the data is that there are deathÌýregistration delays,Ìýparticularly for deaths referred to a coroner. This means some deaths which have already occurred but not been registered will not be included in the analysis.

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Accuracy is about the degree to which the data reflects the real world. This can refer to correct names, addresses or represent factual and up-to-date data.Ìý

The ONSÌýÌýgives information on accuracy and validation checks performed.

CompletenessÌý

Completeness describes the degree to which records are present.Ìý

For a data set to be complete, all records are included, and the most important data is present in those records. This means that the data setÌýcontainsÌýall the records that it should and all essential values in a record are populated.Ìý

Completeness isÌýnot the same asÌýaccuracy as a full data set may still have incorrect values.Ìý

Death certificatesÌýcontainÌýseveralÌýmandatory fields and so the dataÌýused in this reportÌýfor age, sex, place of death, cause of deathÌýand geographical area of deathÌýis highly complete.ÌýAge and sexÌýare 100% complete in provisional data. Geographical area and cause of death are over 99% complete, and place ofÌýdeath isÌýover 98% complete.

UniquenessÌý

Uniqueness describes the degree to which there is no duplication in records. This means that the dataÌýcontainsÌýonly one record for each entity itÌýrepresents, and each value is stored once.Ìý

Some fields, such as National Insurance number, should be unique. Some data is less likely to be unique, for example geographical data such as town of birth.Ìý

Checks are performed by ONS toÌýidentifyÌýand remove duplicateÌýdeath registrations.

ConsistencyÌý

Consistency describes the degree to which values in a data set do not contradict other valuesÌýrepresentingÌýthe same entity. For example, a mother’s date of birth should be before her child’s.Ìý

Data is consistent if itÌýdoesn’tÌýcontradict data in another data set. For example, the date of birth recorded for the same person in 2 different data setsÌýshould beÌýtheÌýsame.Ìý

ONS deaths dataÌýreflectsÌýinformationÌýas recorded on death certificatesÌýbased onÌýinformation provided by the informant, medicalÌýexaminerÌýand coroner (if applicable). Data is notÌýrequiredÌýto match with dataÌýon other records.ÌýThe ONSÌýÌýgives more information on how death certificates are completed.

TimelinessÌý

Timeliness describes the degree to which the data isÌýan accurateÌýreflection of the period that itÌýrepresents, and that the data and its values are up to date.Ìý

Some data, such as date of birth, may stay the sameÌýwhereasÌýsome, such as income, may not.Ìý

Data isÌýtimelyÌýif the time lag between collection and availability isÌýappropriate forÌýthe intended use.Ìý

Deaths are subject to registration delays, meaning that deaths by date of occurrence are alwaysÌýsomewhat incomplete.ÌýONS has published further information onÌýtheÌý.

Based on historic patterns in death registrationÌýdelays, it is expected that when extracting data for this analysis in February 2026,Ìýover 99% of deaths that occurred inÌýwinter 2024 to 2025 have been registered.Ìý

Deaths referred to coronersÌýare subject to longer registration delays, meaningÌýthereÌýisÌýlikely to be a higher proportion of missing deaths among deaths where the underlying cause of death isÌýin the categoryÌý‘ExternalÌýcauses’.

ValidityÌý

Validity describes the degree to which the data is in the range and format expected. For example, date of birth does not exceed the present day and is within a reasonable range.Ìý

Valid data is stored in a data set in theÌýappropriate formatÌýfor that type of data. For example, a date of birth is stored in a date format rather than in plain text.Ìý

The ONSÌýÌýgives information on accuracy and validation checks performed.

Strengths and limitations of theÌýtemperatureÌýdata

The strengths of the data include:Ìý

• a comprehensive record of temperatures measured by the Met Office in England

• validation and data quality checks carried out by the Met Office

The main limitation of the temperature data is the use of an England-wide average temperature when estimating the relationship between temperature and mortality. This might obscure patterns at regional level where a region experienced very different temperatures to the rest of England.

Strengths and limitations of theÌýinfluenza data

The main strength of the influenza data is that it provides a record of influenza circulation across the England population, calculated using reliable methods aligned with other UKHSA publications.

The limitations of the data are:Ìý

• the use of an England-wide average, which might obscure patterns at regional level or in specific subgroups of the population such as older adults

• the data only being available at weekly level

Sound methodsÌý

Statistical outputs should beÌýproducedÌýusingÌýappropriateÌýmethodsÌýand recognised standards.

This section describes how the statistics were produced and quality assured.Ìý

Data set productionÌý

Method for statistical modellingÌýof cold-associated mortality

Estimates of modelled mortality were obtained from a statistical modelÌýbased on the observed temperature-mortality relationship over several recent winters. The winters of 2019 to 2020 and 2020 to 2021 were excluded from modelling, due to the significant impacts of the COVID-19 pandemic on mortality in these periods.

Mortality data for deaths occurringÌýfrom 2013 to 2025 was obtained from ONS (using final annual registrations data for deaths registered 2013Ìýto 2024, and provisionalÌýweeklyÌýregistrations data for deaths registered since the end of 2024).

Temperature data was obtained from the Met Office, using a latitude-longitude grid atÌý0.1 degreeÌýresolution to derive dailyÌýmeanÌýtemperature data at regional levelÌýfor the same period. An overall daily mean temperature for EnglandÌýwas calculated for each date as a population-weighted average.Ìý

Temperature and mortality dataÌýisÌýjoined based on date.ÌýAÌýquasi-Poisson regressionÌýmodel is fitÌýusing the distributed lag non-linear modelling framework, to estimateÌýthe relative risk of mortalityÌýat each temperature. The relative risk is an index representing the risk of mortality relative to a reference temperature: if a temperature has a relative risk of 1.1, this indicates a 10% higher risk of death compared to the reference temperature. A relative risk of 0.9 indicates a 10% lower risk of death compared to the reference temperature. The reference temperature was set at 14.4°C, the Minimum Mortality TemperatureÌý(the temperature at which risk of mortality is lowest) for the England population overall.

95% confidence intervals are also calculated for the relative risk at each temperature, representing uncertainty due to random variation in daily deaths.ÌýExposure-response curves showing the relationship between temperature and mortality and the confidence intervals are presented in the report.

This modelled relationshipÌýis applied to the actual temperatures during the cold episodes of winter 2024 to 2025 to obtain modelled predictions of cold-associated mortality related to each episode.ÌýFurtherÌýÌýis available.ÌýThe model was adapted from the model described in the link above to take account of differences between heat and cold, including:

• increasing the lag to 14 days to account for longer delays between cold weather and mortality impacts
• setting knots at appropriate percentiles of the temperature-mortality relationship
• including an adjustment for the effects of COVID-19, using the number of deaths with COVID-19 recorded on the death certificate on that day as a variable, in line with

Specific parameter settings in the model, such as the location of knots, were determined through running the model with different options and comparing model performance statistics.

Method for calculating estimates adjusted for flu

The temperature-mortality relationship was also modelled with an additional adjustment for the effect of flu.

Data on influenza activity at weekly level was obtained from the UKHSA Vaccines Analysis team. The weekly value was applied uniformly to all days across that week, and joined to the temperature and deaths data based on date. A single parameter was included in the model for the effect of influenza activity, with a lag of up to 2 weeks.

The modelling method described above was repeated with other parameter settings and the reference temperature remaining the same, and the temperature-mortality relationship with and without the adjustment for flu is presented in the report.

Method for comparing recent winters with previous winters

The temperature-mortality relationship was modelled over the latest 5-year period (2018 to 2019, 2021 to 2022, 2022 to 2023, 2023 to 2024 and 2024 to 2025) and a previous 5-year period (2013 to 2014, 2014 to 2015, 2015 to 2016, 2016 to 2017 and 2017 to 2018). The modelling method described above was repeated for both subsets of data with the same parameter settings and reference temperature, and the temperature-mortality relationships in each period are presented in the report.

Methods forÌýbreakdowns of cold-associated mortalityÌý

The number of cold-associated deaths was broken down by region, age group, sex, place of death and cause of death.ÌýThis was done by applying the method for modelled cold-associated mortality above to each subset of the data, using the same parameter settings and reference temperature.

Grouping by region was done based on the Lower Super Output Area (LSOA) of the location of death,ÌýaggregatedÌýto region boundaries. Where location of death was not recorded, the location of residence was used instead.ÌýRates per million population were calculatedÌýusing ONSÌýmid-yearÌýpopulationÌýestimatesÌýby local authority,Ìýaggregated to region boundaries.

Grouping by age was done based on theÌýage at death as recorded on the death certificate.ÌýThis is based on Grouping B of theÌý, but with anÌýadditionalÌýbreakdown of the ‘aged 75Ìýyears and over’Ìýgroup into ‘aged 75 to 84 years’ and ‘aged 85 years and over’. °Õ³ó±ðÌýadditionalÌýdetail for older age groups is usedÌýbecause cold-associated mortality is greatest for older age groups.ÌýGreater uncertainty with small numbers of deathsÌýdoesÌýnot allow using smaller groupings below the age of 65. Rates per million population were calculated using ONSÌýmid-yearÌýpopulationÌýestimates aggregated to the same groups.

Grouping by sex was done based on sex as recorded on the death certificate.ÌýThis is the sexÌýas reported by the informantÌýto the registrar, and may differ from legal sex,Ìýsex as recorded in health records, or gender identity.ÌýDeaths with unknown or other sex recorded were excluded from the breakdown by sex due to small numbers.ÌýRates per million population were calculated using ONSÌýmid-yearÌýpopulationÌýestimates by sex.

Grouping by place of death was doneÌýbased on theÌýcoding of communal establishments on death certificates.ÌýThese were aggregated to the 5 :

• Own residence
• Hospital
• Care home
• Hospice
• OtherÌýplaces

Deaths with unknown place were grouped with ‘OtherÌýplaces’.ÌýRates per million population are not applicable to grouping by place of death.

Grouping by cause of death was done using the underlying cause of death in the ONS deaths data set. This isÌýdeterminedÌýbyÌýinternationally standardised rules, the Multi-causal and Uni-causal Selection Engine [MUSE] 5.8,Ìýfor assigning an underlying cause of death based on all causes of death recorded.ÌýMore information is available in the ONSÌý.

Causes were aggregatedÌýinto the policy-relevant groups in the report using the followingÌýInternational Classification of Diseases, Tenth Revision (ICD-10) codes:Ìý

• C00 to C97 for cancer
• F01, F03 and G30 for Dementia and Alzheimer’s
• I00 to I99 for all circulatory diseases
• J09 to J18 for influenza and pneumonia
• J40 to J47 for chronic lower respiratory diseases
• S00 to Y98 for external causes

Deaths with any other cause or unknown cause were grouped as ‘All other’.ÌýRates per million population are not applicable to grouping by cause.

Quality assuranceÌý

°Õ³ó±ðÌýreport is produced using R. The production of the figures and the supplementary data tables has been automated, reducing the risk of human error.ÌýThe code is version-controlled using Git andÌýfollowsÌý. The code is independently reviewed by an analyst outside the production team, in line with Aqua Book recommendations. The code is run by a second analyst to check that outputs are reproducible. Further quality assurance is done after running the code.ÌýInterim and final outputsÌýare sense-checked,ÌýandÌýfigures and tables are compared with those in relatedÌýreports by at least 2 members of the production team.

Confidentiality and disclosure controlÌý

Personal and confidential data is collected, processed, and usedÌýin accordance withÌýtheÌýUKHSA Privacy Notice.ÌýAll UKHSA staff with access to personal or confidential information must complete mandatory information governance training, which must be refreshed every year.ÌýInformation is stored on computer systems that are kept up to date and regularly tested to make sure they are secure and protected from viruses and hacking. UKHSA staff do not store data on their own laptops or computers. Instead, data is stored centrally on UKHSA servers.Ìý

Data presented in the ‘Cold mortality monitoring report’ does not relate to individuals. The figures reported areÌýthe modelled number of deaths that can be attributed to cold weather, based on the relationship between cold temperatures and mortality over several winters. There is no risk of including data whichÌýidentifiesÌýan individual.

GeographyÌý

All information in the report is presented for England overall. Some information is also provided at regional level.

Quality summaryÌý

QualityÌýmeans that statistics fit their intended uses, are based onÌýappropriate dataÌýand methods, and are not materially misleading.Ìý

Quality requires skilled professional judgement about collecting, preparing, analysing, and publishing statistics and data in ways that meet the needs of people who want to use the statistics.Ìý

This section assesses the statistics against theÌýÌýdimensions of quality.

RelevanceÌý

Relevance is the degree to whichÌýthe statistics meet user needs in both coverage and content.Ìý

The statistics are relevant for public health professionals and across government. Results are used for national risk assessmentÌýsuch asÌýtheÌýNational Risk RegisterÌýfor low temperatures and snow, and to develop understanding ofÌýtheÌýmost vulnerable populations in and followingÌýcold episodes. This informsÌýguidance and public health action to protect vulnerable groups.

The report contributes to UKHSA fulfilling obligations by reporting on cold-associated deaths to directly support delivery of the Weather-Health Alerts, a core responsibility of UKHSA under the Civil Contingencies Act 2004 as a category 1 responding organisation, and the monitoring of Goal 2 of Adverse Weather and Health Plan.

Accuracy and reliabilityÌý

Accuracy is the proximity between an estimate and the unknown true value. Reliability is the closeness of early estimates toÌýsubsequentÌýestimated values.Ìý

Cold-associated deaths can potentially be underestimated due to registration delays.ÌýThis is unlikely to have a significant impact as almost all deaths occurring in the period modelled will have been registered prior to the analysis.

There is some uncertainty in the figures as cold-associated deaths cannot be measured directlyÌýbutÌýonly estimated through modelling.ÌýWe thereforeÌýreport 95% confidence intervals around estimates. There is some additional uncertainty in the figures adjusted for flu, due to the flu data only being available at weekly and national level.

Timeliness and punctualityÌý

Timeliness refers to the time gap between publication and the reference period. Punctuality refers to the gap betweenÌýplanned and actual publication dates.Ìý

The first cold mortality report on winter 2024 to 2025 is being published during the following winter, approximately 11 months after the end of the reference period, November to March of the previous year. Future annual reports will be published in the autumn, with the next report on winter 2025 to 2026 anticipated to be published in autumn 2026. This isÌýtimelyÌýas it is before the beginning of the newÌýCHAÌýseason and allows time for the results of the report to inform preparedness and planning for the next winter.Ìý

°Õ³ó±ðÌýannualÌýreportsÌýareÌýofficial statisticsÌýand are pre-announced at least 28 days in advance. Provisional publication dates for the year ahead are pre-announced online in December and can be found on theÌýUKHSA release calendar.

Accessibility and clarityÌý

Accessibility is the ease with which users can access the data, also reflecting the format in which the data is available and the availability of supporting information. Clarity refers to the quality and sufficiency of the metadata,ÌýillustrationsÌýand accompanying advice.Ìý

Tables and visualisations in the reportÌýmeet accessibility standards.

Coherence and comparabilityÌý

Coherence is the degree to whichÌýdata that is derived fromÌýdifferent sourcesÌýor methods, but refer to the same topic, is similar. Comparability isÌýthe degree to which data can be compared over time and domain.

UKHSA has also produced estimates of cold-associated mortality in each winter through the annual influenza surveillance reports, using a different modelling method and definition of cold weather.

The influenza reports use weekly data, and only consider the impact of extremely cold weeks on mortality, defined as an entire week with average temperature below 3°C. This ‘Cold mortality monitoring report’ instead provides an estimate of the impact of cold weather episodes on mortality, including some more moderate or shorter periods of cold. The definition in the ‘Cold mortality monitoring report’ (2 consecutive days below 2°C) is chosen for its relevance to CHA thresholds. Despite these differences, during methodological development of this report, estimates from the 2 methods were found to be coherent.

ONS published a one-off report on as experimental statistics. Within this report an estimate of cold-attributable mortality for each year within the series was provided.

The estimates produced in this analysis are not directly comparable with those published by ONS, as the 2 approaches are designed to assess different aspects of cold-related mortality. This report focuses on deaths occurring during specific cold weather episodes in recent winters linked to CHA thresholds. It uses methods that capture short-term delayed effects and allow for disaggregation according to factors such as influenza, age, sex, region, place of death, and underlying cause. As a result, the estimates reflect current patterns of risk and vulnerability under today’s climate, population, and health system conditions. In contrast, the ONS estimates are based on a much longer time period and describe the average effect of cold weather over 35 years, using a statistical definition of cold days applied across the whole period.

The ONS results therefore provide a long-term, smoother picture of cold-related mortality, while the findings presented in the ‘Cold mortality monitoring report’ describe the estimated impacts of specific recent cold weather episodes. The figures should be interpreted in the context of their different aims and cannot be compared directly.

Uses and usersÌý

Users of statistics and data should be at the centre of statistical production, and statistics should meet user needs.

This section explains how the statistics are used, and how we understand user needs.Ìý

Appropriate use of the statistics

These statistics can be usedÌýto:Ìý

• monitor national annual trends in cold episode days andÌýcold-associatedÌýdeaths

• compare cold-associated deaths between different areas, population groups,ÌýsettingsÌýand causes

• monitor progress against the goals of the UKHSA Adverse Weather and Health PlanÌýto reduce mortality related to adverse weather.

KnownÌýusersÌý

Public health and emergency planning professionals, at national,ÌýregionalÌýand local levels.Ìý

UK Government bodies involved in monitoring of climate change adaptation and impacts on health as a result of adverse weather events.

User engagementÌý

We are carrying out a user survey to gather your views and shape the future of our publication.

In addition, engagement will focus on proactive communication and targeted user engagement to support clear understanding and appropriate use of the statistics upon release. This will include coordinated press activity, publication of blogs to explain the purpose, methods and limitations of the output, and early engagement with regional and local stakeholders. Two-way feedback will be supported through regional networks and direct engagement with stakeholders, alongside wider dissemination through established forums and conferences. Ongoing methodological scrutiny and peer review will support continuous improvement and alignment with the Code of Practice for Statistics.

We also informally collect and review user feedback. Our user engagement activities include:

• stakeholder webinarsÌýsuch asÌýannual summer preparedness and winter preparedness

• reviews of queries received by the Extreme Events and Health Protection team from stakeholders

• stakeholder workshops on climate-health metrics development run by the UKHSA Centre for Climate and Health Security

Related statisticsÌý

Annual surveillance of influenza and other seasonal respiratory viruses in the UK reports on mortality associated with influenza, COVID-19 and cold weather. This report relies on weekly data and focuses only on extremely cold weeks, defined as a full week where the average temperature is below 3°C. In contrast, this ‘Cold mortality monitoring report’ uses daily data and looks at defined cold weather episodes, including shorter or less extreme periods of cold. This approach aligns with Cold Weather Alert thresholds and allows us to capture impacts that might be missed if we only looked at very severe, prolonged cold. Despite these differences, the results from the 2 approaches are broadly consistent.

In 2023 ONS published . This analysis provides an estimate of the average impact of cold weather over 35 years. The ‘Cold mortality monitoring report’ instead focuses on recent winters and estimates the impact of specific cold weather episodes under today’s conditions, including today’s population structure, health system pressures, and patterns of vulnerability. The ONS work provides a long-term, broad view of average cold-related mortality over decades, while this report provides a more detailed and up-to-date picture of the impact of recent cold spells. Because the methods and purposes differ, the figures should not be directly compared, but together they provide complementary insights into how cold affects health in England.

The annual heat mortality monitoring reports provide information on deaths observed during heat episodes each year to inform public health actions. This differs to the ‘Cold mortality monitoring report’, which is based on statistical modelling of how mortality has responded to low temperatures over 5 recent winters.

The weekly all-cause mortality surveillance reports compare the actual number of deaths in England compare with the expected numbers of deaths for each week.

Other assessments of mortality include the , which is published by ONS.

The Office for Health Improvement and Disparities also produces the Excess mortality within England report, which provides estimates of expected deaths by month of registration for population subgroups and by cause of death.

The different methods used in the UK for mortality assessment, and their varied purposes, are discussed in more detail in Measuring excess mortality: a guide to the main reports.