Non Covid-19 deaths by occupation – a closer look

Non Covid-19 deaths by occupation – a closer look

Non Covid-19 deaths by occupation – a closer look

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ONS data raises important questions about non COVID-19 deaths by occupation

Why have non COVID-19 related deaths in the hairdressing industry risen by 30%?

Following a freedom of information request on the 25th January 2021, The Office of National Statistics (ONS) released the dataset: Coronavirus (COVID-19) related deaths by occupation, England and Wales. [1]

The summary accompanying the dataset concluded that “those working in close proximity to others continue to have higher COVID-19 death rates when compared with the rest of the working age population.” [2]

This data is clearly vital in understanding the impact of lockdown legislation on COVID-19 deaths and informs the growing conjecture about the disease’s disproportionate impact on workers with low, or irregular incomes.

Without doubt, we are fortunate in this country that the ONS provides such valuable insight to enable us to make sense of what is happening. However, the summary drew no conclusions about the increases in non COVID-19 related deaths by occupation, prompting the author to take a closer look. It highlighted a worrying increase in non COVID-19 deaths in one particular occupation – hairdressing.

Delving deeper into the deaths by occupation data

The ONS dataset provides context to the deaths including COVID-19 against the average “expected” deaths over the same period for the past five years. [3]

The main media commentary following the release of the dataset focused on the fact that more men than women of working age had COVID-19 recorded on their death certificates. Overall, the excess deaths for women in the period covered by the dataset was 1,891. The deaths of women attributed to COVID-19 was 1,742, so no significant statistical difference. However, that total figure hides a range of outcomes across the 369 occupations listed in the dataset. When you look at the dataset in more detail some interesting numbers emerge.

In Table 1 at the end of this article (adapted from table 9 of the ONS report), I have added two extra columns: Non COVID-19 excess mortality 2020; and Percentage change Non COVID-19 excess mortality 2020.

At the “top” of the table, now sorted by percentage of non COVID-19 deaths, are hairdressers with an increase in Non COVID-19 excess mortality of 30%. But what accounts for such a marked increase and what are the leading causes of these excess deaths?

Delving deeper still – some concerning increases in several causes of death of hairdressers

Following a request for more detailed information on the mortality rate of the “top” group – hairdressers – the ONS responded very promptly on the 12th February, publishing a new dataset breaking down the leading causes of death. [4]

The total deaths, for men and women, was 398, an increase of 37% compared with the average number of deaths over the past five years covering the same reporting period. COVID-19 accounts for 20 of those deaths.
Table 2 at the end of this article (adapted from table 1 of the second ONS report) shows the top ten causes of death (out of 63) showing dramatic increases in suicide and accidental poisoning among hairdressers, as well as a startling rise in deaths from breast cancer and strokes.

Questions we should ask next

This paper was specifically written to draw attention to a trend overlooked by most commentary on the original dataset release, namely a steep rise in non COVID-19 related deaths in certain professions, and in particular hairdressers.

As more datasets are released covering longer periods of time, new trends in the data will become apparent. It is still too early to draw definite conclusions, and whilst we must always be careful to remember that correlation does not imply causation, these datasets do raise the imperative to ask more questions such as:

  • Why is it that, during this pandemic, COVID-19 was responsible for less than 7% of the 37% increase of deaths in hairdressers?
  • What is driving the increase in nine of the top ten causes of deaths among hairdressers?
  • Breast cancer deaths among hairdressers are up by 44%. Is this figure an outlier, if not, what is driving this increase?
  • What is behind the doubling of deaths from strokes among hairdressers?
  • Deaths from suicide and accidental poisoning are up nearly 50%, and together, are more than double the deaths from COVID-19. Why?

Increased deaths across this many categories in a single occupation cannot simply be dismissed as an outlier, or a one-off event. There will almost certainly be an underlying cause.

Many hairdressers are self-employed and have been unable to work for long periods since March 2020. A lot of money was spent by these businesses to make their salons safe when they reopened after the first lockdown.

There has been a lot of recent commentary in the media about how many excess deaths may have been caused as a result of the lockdown policies. Is this an early indicator of this effect? Certainly, the rises in accidental poisoning and suicides in this – generally low paid – occupation is extremely worrying.

The original dataset, published in January, lacked the context of the occupation size and the median income of each occupation. Obtaining these additional data elements may tell us more about the anecdotal evidence that it is the poor, or those with irregular incomes, who are suffering disproportionately from the lockdown. Perhaps the ONS will add these data fields to the next release.

Hopefully, the NHBF, the trade body for hairdressers, will also study this dataset in more detail and work with their membership to reduce some of the tragic, avoidable deaths in these categories.

Acknowledgement: Open data and the Office for National Statistics

We are very fortunate to have the ONS and an open data policy in the UK. I would like to thank the ONS for their prompt response to my request, and the great work they do in regularly publishing datasets that allow us to examine for ourselves what is really happening. This open data policy allows anyone to delve beyond the headlines we see every day.


Table 1: Deaths for women by occupation involving ten or more instances of COVID-19

Table 2: Top 10 causes of death among hairdressers



[2] “Today’s analysis shows that jobs with regular exposure to COVID-19 and those working in close proximity to others continue to have higher COVID-19 death rates when compared with the rest of the working age population. Men continue to have higher rates of death than women, making up nearly two thirds of these deaths.”

Ben Humberstone, ONS, Head of Health Analysis and Life Events, 25th January 2021

[3] The dataset covers deaths involving COVID-19 and all causes by sex (those aged 20 to 64 years), England and Wales, for deaths registered between 9th March and 28th December 2020.

Deaths are defined using the International Classification of Diseases, 10th Revision (ICD-10). Deaths involving COVID-19 include those with an underlying cause, or any mention, of ICD-10 codes :

  • U07.1 (COVID-19, virus identified) or
  • U07.2 (COVID-19, virus not identified).

All causes of death is the total number of deaths registered during the same time period, including those that involved COVID-19.

Table 9 in the dataset breaks the figures down by occupation. Occupation is defined using the Standard Occupation Classification (SOC 2010). The table lists 369 occupations. Table 9 breaks the dataset down further by male and female.
The three columns of figures supplied in the dataset are titled:

  • Deaths involving COVID-19;
  • All causes of death;
  • Average all-cause mortality (2015 to 2019)


About the author

Peter Eales is chair of KOIOS Master Data, a provider of cloud-based data quality software. KOIOS also provides data quality consultancy and training services based on International Standards for data quality. Peter is an internationally recognised expert in the field of characteristic data exchange, and industrial data quality. Peter is a member of a number of International Organization for Standardization (ISO) working groups drafting International Standards in these areas.  

Peter has a daughter who is a self-employed hairdresser

Contact us

+44 (0)23 9387 7599

Non Covid-19 deaths by occupation – a closer look

COVID-19: Numbers have meaning

COVID-19: Numbers have meaning

One of the benefits of data that is compliant with ISO 8000 is that the data is exchangeable without loss of meaning. For those of us involved in data quality, the current flurry of data published about the COVID-19 pandemic is throwing up some familiar failings.

The media is taking lots of data from various sources and turning it into information in an effort to help the general public understand what is happening. However, a number of media outlets are not reporting the definition of a particular figure accurately. As an example, the identical single figure for deaths is being reported in the same publications as being both “from” COVID-19, and “with” COVID-19. Two entirely different concepts.

When this pandemic is over there will be lots of people trying to make sense of the data. One of the likely measures will be deaths per 1m of the population. Comparing total deaths to a fixed measure of the population will give a better sense of the effects than simply using the figure for total deaths by itself.

This article is not attempting to analyse the current data. The purpose of the article is to explain why looking at a single figure and drawing conclusions is not giving the complete picture, why understanding the definitions behind each data element is important, and, in the case of England and Wales, how the data is collected.

Is the data fit for purpose?

Fortunately, in England and Wales the Office of National Statistics (ONS) follows good practice.

In data standards we talk about the quality of data being defined as its “fitness for purpose”. More specifically in the case of ONS data, it is the fitness for purpose with regards to the European Statistical System dimensions of quality:

  • relevance – the degree to which a statistical product meets user needs in terms of content and coverage;
  • accuracy and reliability – how close the estimated value in the output is to the true result;
  • timeliness and punctuality: the time between the date of publication and the date to which the data refers, and the time between the actual publication and the planned publication of a statistic;
  • accessibility and clarity – the ease with which users can access data, and the quality and sufficiency of metadata, illustrations and accompanying advice:
  • coherence and comparability – the degree to which data derived from different sources or methods, but that refers to the same topic, is similar, and the degree to which data can be compared over time and domain, for example, geographic level;

and two other important dimensions:

  • output quality trade-offs; and
  • assessment of user needs and perceptions.

The ONS issues comprehensive data sets free of charge so that detailed analysis can be carried out by third parties.

How the ONS report deaths in England and Wales

The ONS produce summary information on annual deaths. The following table lists the number of deaths each year in England and Wales between 2014 and 2018.

The general rise in these figures is something to bear in mind when reviewing the average number of deaths during this period when compared to the five-year average, and is another reason why using the number of deaths per million gives better context to the figures. According to the ONS, the population of England and Wales in mid-2014 was 57,408,600, and in mid-2018 it was 59,115,809, an increase of 1,707,209. When the mid-2018 data set was published, the ONS noted that “Since mid-2000, the population of the UK has grown by almost 7.5 million and there are 2.4 million more people aged 65 to 84 years and 489,000 more aged 85 years or over.” Annual population updates are normally published by the ONS in the last week of June.

The ONS also records “excess winter deaths”. In the 2018 to 2019 winter period (December to March), there were an estimated 23,200 EWD in England and Wales. This was substantially lower than the 49,410 EWD observed in the 2017 to 2018 winter and lower than all recent years since 2013 to 2014 when there were 17,280 EWD.

How the ONS is reporting deaths involving COVID-19

As a result of the current pandemic, the ONS currently provides a separate breakdown of the numbers of deaths involving COVID-19. That is, where COVID-19 or suspected COVID-19 was mentioned anywhere on the death certificate, including in combination with other health conditions. If a death mentions COVID-19, it will not always be the main cause of death, it will sometimes be a contributary factor. The conditions mentioned on the death certificate are used to derive an underlying cause of death.

Mortality statistics in England and Wales are derived from the registration of deaths certified by a doctor or a coroner. Deriving conditions from a death certificate introduces a known variable; the accuracy of the data drawn from the certificate is dependent on the doctor completing the certificate. Before submitting a death registration through the Registration Online (RON) system, the registrar will verify that all the information provided has been entered accurately. There are some automatic validation checks within RON to help the registrar with this process. The cause of death reported represents the final underlying cause of death. This takes account of additional information received from medical practitioners or coroners after the death has been registered.

The authoritative source in England and Wales for the certification of births, marriages, and deaths is the General Register Office (GRO). The GRO pass death registration information to the ONS electronically, and the ONS: codes; compiles; and publishes these figures weekly.

The ONS short list for cause of death is based on a standard tabulation list developed in consultation with the Department of Health. This list of over 100 conditions was based on the following:

  • all conditions given in the World Health Organization (WHO) basic tabulation list; with the exception of a few conditions that are so rare as certified causes of death in England and Wales that they could safely be excluded from the list;
  • totals for each International Classification of Diseases, Tenth Revision (ICD-10) chapter;
  • conditions used in monitoring public health targets;
  • other conditions often cited by ONS.

Currently, in the UK The Department of Health and Social Care (DHSC) release daily updates on the GOV.UK website counting the total number of deaths reported to them that have occurred in hospitals among patients who have tested positive for the coronavirus (COVID-19) up until 5pm the day before.

Since 2 April, NHS England have been releasing daily updates of deaths in hospitals among patients who have tested positive for COVID-19 in England, which includes updates on previous days numbers.

The Office for National Statistics (ONS) provides figures based on all deaths registered involving COVID-19 according to death certification, whether in or out of hospital settings.

At the time of publication, further work is in progress across government to reconcile all sources of COVID-19 deaths data, but as you can see these figures are not directly comparable.

The figures produced by the ONS are about two to three weeks behind the daily reported deaths.

There has been a lot of speculation regarding the number of deaths in care homes. The ONS publishes this data.

There has also been speculation regarding whether influenza and pneumonia deaths are recorded each year. They are:

One point to note here is that the reporting of deaths involving COVID-19 is more comprehensive than the previous reporting of respiratory infections, so any comparison of these figures should make this point.

The England and Wales ONS is very clear on how it compiles the information it publishes. Authoritative data sources are always the best places to go start your search for data. Authoritative sources are normally very clear about the definitions for the data they publish, and very open about the methodologies they use. I was visiting the Korean Data Agency in Seoul at the start of this outbreak, and saw first hand how they operate. The ONS is very open, their website ( is a mine of useful information, and much of the text in this article describing how the numbers have been derived has been drawn from various parts of that site.

Social statisticians studying this mortality figures from pandemic will have lots of data to analyse in the future. They will also look at comparing population density rates as well as the number of deaths per million. They will also adjust the figures for the variability of testing – in both quantity and quality of the tests. The total mortality figures you read about now will have more context in the future.

How is COVID-19 data recorded where you are?

I would be very interested to hear from my fellow data experts in other countries, as to how their deaths are being recorded and published by their data agencies, and if their officially published figures for COVID-19 include deaths “involving COVID-19” where COVID-19 is not the main cause of death. Looking at the deaths per million of population figures published to date, there are some interesting variances that may be explained by the way the data is collected.

When the data is analysed in the coming years, we will need detailed quality data with clear definitions. There will be lots of variances, certain types of deaths will rise, others will fall. We will need to make sure we are comparing like numbers before we draw conclusions. This is vital to enable accurate decisions to be made about managing, or even preventing future pandemics.

About the author

Peter Eales is a subject matter expert on MRO (maintenance, repair, and operations) material management and industrial data quality. Peter is an experienced consultant, trainer, writer, and speaker on these subjects. Peter is recognised by BSI and ISO as an expert in the subject of industrial data. Peter is a member ISO/TC 184/SC 4/WG 13, the ISO standards development committee that develops standards for industrial data and industrial interfaces, ISO 8000, ISO 29002, and ISO 22745. Peter is the project leader for edition 2 of ISO 29002 due to be published in late 2020. Peter is also a committee member of ISO/TC 184/WG 6 that published the standard for Asset intensive industry Interoperability, ISO 18101.

Peter has previously held positions as the global technical authority for materials management at a global EPC, and as the global subject matter expert for master data at a major oil and gas owner/operator. Peter is currently chief executive of MRO Insyte, and chairman of KOIOS Master Data.

KOIOS Master Data is a world-leading cloud MDM solution enabling ISO 8000 compliant data exchange

MRO Insyte is an MRO consultancy advising organizations in all aspects of materials management

What is K:spir and how can it revolutionise the SPIR process?

What is K:spir and how can it revolutionise the SPIR process?

What is K:spir and how can it revolutionise the SPIR process?

The SPIR process urgently needs to enter the 21st century

At KOIOS Master Data we have a unique understanding of the difficulties caused by the current SPIR (Spare Parts Interchangeability Record) process. Through our team’s years of MRO consultancy work, we have first-hand experience of how damaging the poor-quality data supplied in SPIRs can be to oil and gas projects. It can have a profound effect on cost, time and resource – cost, time and resource that could be spent innovating and developing a competitive advantage. Not to mention, the unnecessary wastage it can lead to, in an industry that can hardly accommodate it in the current climate. In this age of Industry 4.0, digital transformation and international data standards such as ISO 8000, the question begs – why is data quality consistently letting the side down? When we struggled to find an effective SPIR solution, KOIOS Master Data was born and we set out to create one.

K:spir is the only SPIR software designed this century using ISO 8000 standard data. It creates machine-readable data that retains quality throughout the chain, enabling accurate decision making and resulting in reduced cost, time and resource.

Here, we look at the importance of master data management, the challenges created by the SPIR process, and how K:spir is uniquely positioned to resolve those challenges.

Why is data management so important to the SPIR process?

In this age of ‘data explosion’, most businesses are aware of how poorly-managed data can put them on the back foot. In Experian’s 2019 Global Management Data Research, they found that 95% of organizations surveyed see a negative impact from poor data quality.

Similarly, the Aberdeen Group’s Big Data Survey in 2017 found that the biggest challenges for Executives arise from data disparity, including inaccessible data, poor quality data informing decisions and the growing need for faster analysis. 

The overall effect is a lack of trust in data, to the great detriment of strategic decision making. And when you can’t trust your data to inform business decisions, then cost, time and resource will inevitably suffer.

In the context of the SPIR process, accurate decision making is everything. The SPIR exists as a tool for forecasting spares requirements for the life of a project, its sole purpose being to assist the Owner Operator (O/O) to make accurate decisions. Yet, as many will attest, the data supplied is often inaccurate, hard to access and sometimes supplied by the Engineering Procurement Contractor (EPC) at handover, by which time it is often too late to inform anything at all. 

Experts have raised the question – if you can’t trust SPIRs to make accurate procurement decisions, then are they worth the paper they’re written on?. The process is clearly out-of-date, yet it continues to blight the efficiency of many oil and gas upstream projects.

SPIRs dissected 

The shortcomings of the antiquated SPIR process can be summarised into three key areas:


SPIRs are generated from paper forms and are transcribed many times, so part descriptions become distorted. Often, parts have multiple descriptions.

Solution: K:spir locks in data quality right at the start of the process, using ISO 8000 standard data. Part descriptions are consistent and safe from misinterpretation, providing confidence in forecasting and reordering. 

SPIRs are usually completed by an Original Equipment Contractor (OEM), who is not necessarily aware of the O/O’s operating and maintenance procedures. Therefore, they do not take into account equipment criticality or maintenance capability.

Solution: K:spir uses the maintenance and repair strategy to determine the spares requirement, reducing wastage and taking cost off of the bottom line.


SPIRs often provide information in spreadsheets or pdfs, which are impossible to extract data from quickly, if at all. To extract anything meaningful is very cost and time-intensive, and relies on support from IT specialists.

Solution: K:spir provides instant reporting on the completeness and cost of spares, allowing for accurate decision making. The information is fully configurable to the requirements of the O/O. It can also create a Maintenance Bill of Materials (BoM) and is interoperable with maintenance systems.

Information is not portable and has to be re-entered for different systems.

Solution: K:spir generates portable (machine-readable) data saving significant time spent re-keying information and unnecessary data handling costs.

Data exists on many platforms and is not available to all stakeholders, all of the time.

Solution: K:spir is cloud-based, providing simultaneous access to all stakeholders in the chain. This allows for more transparency and accountability at all stages of the project lifecycle.


Sometimes even as late as handover, by which time it’s too late for the O/O to minimize the operating risk. There is no opportunity to make informed decisions, such as ordering spares with long lead times, or calculating warehouse space. This can lead to unnecessary wastage and operational difficulties along the line.

Solution: K:spir provides transparency right from the beginning of the project, allowing for critical decisions to be made early on. 

With its unique set of features and benefits, it’s clear that K:spir can relieve the symptoms of the current SPIR process with immediate effect, saving valuable cost, time and resource.

A SPIR – this is not what efficiency looks like!

SPIRs and effective MDM – who is responsible for getting it right?

As confident as we are in the KOIOS software suite to advance the world of Master Data Management (MDM), there are clearly other factors that need to be addressed, most notably, ownership. It is a thorny area, and one that is being more keenly contested as digital transformation rattles on apace. As the Aberdeen Group puts it, there is a “growing urgency for better data management”, as businesses see the shortfalls of their inability to harness data.

Experian’s report shows that in 84% of cases, data is still managed primarily by IT departments. Revealingly, 75% of their sample thought that ownership should lie within the business, with support from IT. They conclude that organizations should develop their MDM strategy to fulfill the needs of a much larger group of stakeholders, who wish to harness the power of their data to improve decision making and efficiency.

In the context of SPIRs and oil and gas projects, we believe that O/Os should become more demanding over the quality of data supplied to them by manufacturers. It is unrealistic for their IT experts to have sight of the broader operational requirements, with their own priorities being diverse and demanding. It is the Executives who suffer the consequences of the risk taken by ignoring poor data, and the operations and maintenance departments that will experience the pain. Clearly, they need to make their voices heard much earlier in the process. That said, manufacturers and EPCs also need a better understanding of the challenges faced by O/Os, and in our view should share the responsibility for getting the data right from the start.

It is, as previously stated, a tough subject, but we are constantly encouraged by the conversations we have with manufacturers and O/Os alike. More and more key stakeholders are waking up to the power that effective MDM can have in driving business forwards, by freeing up cost, time and resource and supporting strategic decision making. Not just to their own ends, but for industry as a whole to fully realize its digital transformation goals.

Join us in our vision to revolutionize the SPIR process

A radical change to the SPIR process and MDM as a whole is on the horizon. While there may be no silver bullet, we firmly believe that the right software is an essential move forward. The KOIOS software suite is geared towards this larger shift in MDM, but in the case of K:spir, the results can be felt immediately.

Our hope is that O/O’s and manufacturers alike will unite in becoming more discerning and demanding about data quality, working as one to create harmony along the chain. At KOIOS Master Data, we are committed to leading the conversation and driving better data quality.

Contact us

If you wish to become part of the change and join us in our vision to revolutionize the SPIR process, we would love to discuss it further with you. 

+44 (0)23 9387 7599

The new paradigm for managing product master data

The management of product master data is having a revolution. The excellent data quality standards ISO 22745 and ISO 8000 from the International Organization for Standardization (ISO) in Geneva, Switzerland,  have changed everything.

In order to adapt organisations need to adopt a new mindset, new tools, new processes, and importantly, people need education and training. Getting this right will lead to significant productivity improvements and an array of other benefits that include: more accurate ordering and a reduction in purchase errors, less operational downtime hunting for the source of supply for spares, greater detail and consistency of product data on eCommerce web sites, shared product specifications throughout the supply chain, less exposure to fraud and counterfeiting through the use of authorized legal identifiers and many more.

Data cleaning is now dead, as is the use of noun-modifiers to define product specifications. Cataloguing at source is the new paradigm. The best entity to describe a product is the manufacturer who designs and builds it, and their product data should be used throughout the supply chain. Doing so means everyone in the supply chain can share the correct product data; load it into their ERP, eCommerce, and/or Punch-out systems; order the right parts from the right supplier at the right time; and cut out expensive, and often inaccurate, data cleaning work. It means purchasing errors are significantly reduced, or eliminated entirely, and the risk of downtime whilst spares are sourced minimized.

The charts below lays out the key success factors organisations need to implement in order to benefit fully . Find out more at