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

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Data quality: How do you quantify yours?

Data quality: How do you quantify yours?

Data quality: How do you quantify yours?

Being able to measure the quality of your data is a vital to the success of any data management programme. Here, Peter Eales, Chairman of KOIOS Master Data, explores how you can define what data quality means to your organization, and how you can quantify the quality of your dataset.

In the business world today, it is important to provide evidence of what we do, so, let me pose this question to you: how do you currently quantify the quality of your data?

If you have recently undertaken an outsourced data cleansing project, it is quite likely that you underestimated the internal resource that it takes to check this data when you are preparing to onboard it. Whether that data is presented to you in the form of a load file, or viewed in the data cleansing software the outsourced party used, you are faced with thousands of records to check the quality of. How did you do that? Did you start by using statistical sampling? Did you randomly check some records in each category? Either way, what were you checking for? Were you just scanning to see if it looked right?

The answer to these questions lies in understanding what, in your organization, constitutes good quality data, and then understanding what that means in ways that can be measured efficiently and effectively.

The Greek philosophers Aristotle and Plato captured and shaped many of the ideas we have adopted today for managing data quality. Plato’s Theory of Forms tells us that whilst we have never seen a perfectly straight line, we know what one would look like, whilst Aristotle’s Categories showed us the value of categorising the world around us. In the modern world of data quality management, we know what good data should look like, and we categorise our data in order to help us break down the larger datasets into manageable groups.

In order to quantify the quality of the data, you need to understand, then define the properties (attributes or characteristics) of the data you plan to measure. Data quality properties are frequently termed “dimensions”. Many organizations have set out what they regard as the key data quality dimensions, and there are plenty of scholarly and business articles on the subject. Two of the most commonly attributed sources for lists of dimensions are DAMA International, and ISO, in the international standard ISO 25012.

There are a number of published books on the subject of data quality. In her seminal work Executing Data Quality Projects: Ten Steps to Quality Data and Trusted Information™ (Morgan Kaufmann, 2008), Danette McGilvary emphasises the importance of understanding what these dimensions are and how to use them in the context of executing data quality projects. A key call out in the book emphasises this concept.

“A data quality dimension is a characteristic, aspect, or feature of data. Data quality dimensions provide a way to classify information and data quality needs. Dimensions are used to define, measure, improve, and manage the quality of data and information.
The data quality dimensions in The Ten Steps methodology are categorized roughly by the
techniques or approach used to assess each dimension. This helps to better scope and plan a project by providing input when estimating the time, money, tools, and human resources needed to do the data quality work.

Differentiating the data quality dimensions in this way helps to:
1) match dimensions to business needs and data quality issues;
2) prioritize which dimensions to assess and in which order:
3) understand what you will (and will not) learn from assessing each data quality dimension, and:
4) better define and manage the sequence of activities in your project plan within time and resource constraints”.

Laura Sebastian-Coleman in her work Measuring Data Quality for Ongoing Improvement, 2013 sums up the use of dimensions as follows:

“if a quality is a distinctive attribute or characteristic possessed by someone or something, then a data quality dimension is a general, measurable category for a distinctive characteristic (quality) possessed by data.

Data quality dimensions function in the way that length, width, and height function to express the size of a physical object. They allow us to understand quality in relation to a scale or different scales whose relation is defined. A set of data quality dimensions can be used to define expectations (the standard against which to measure) for the quality of a desired dataset, as well as to measure the condition of an existing dataset”.

Tim King and Julian Schwarzenbach in their work, Managing Data Quality – A practical guide (2020) include a short section on data characteristics, that also reminds readers that when defining a set of (dimensions) it depends on the perspective of the user; back to Plato and his Theory of Forms from where the phrase “beauty lies in the eye of the beholder” is derived. According to King and Schwarzenbach quoting DAMA UK, 2013, the six most common dimensions to consider are:

  • Accuracy
  • Completeness
  • Consistency
  • Validity
  • Timeliness
  • Uniqueness

The book also offers a timely reminder that international standard ISO 8000-8 is an important standard to reference when looking at how to measure data quality. ISO 8000-8 describes fundamental concepts of information and data quality, and how these concepts apply to quality management processes and quality management systems. The standard specifies prerequisites for measuring information and data quality and identifies three types of data quality: syntactic; semantic; and pragmatic. Measuring syntactic and semantic quality is performed through a verification process, while measuring pragmatic quality is performed through a validation process.

In summary, there is plenty of resource out there that can help you with understanding how to measure the quality of your data, and at KOIOS Master Data, we are experts in this field. Give us a call and find out how we can help you.

Contact us

In summary, there is plenty of resource out there that can help you with understanding how to measure the quality of your data, and at KOIOS Master Data, we are experts in this field. Give us a call and find out how we can help you.

+44 (0)23 9387 7599

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

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