Maximum cigarette pack size: a neglected aspect of tobacco control

Written by Anna Blackwell, Senior Research Associate

Follow Bristol Tobacco and Alcohol Research Group (TARG) on twitter

The manufacturing or importing of packs of cigarettes with fewer than 20 cigarettes per pack was prohibited in the UK when the EU Tobacco Products Directive and standardised packaging legislation were fully implemented in May 2017. This change was aimed at reducing the affordability of cigarettes and thereby discouraging young people from smoking. This directive also required the removal of branding and established a standard shape and dark green colour for packaging, including pictorial health warnings, which prevented the use of packaging for promotion and reduced its appeal.

However, the tobacco industry has been able to exploit loopholes in recent packaging regulations, including the absence of a regulated maximum pack size, by introducing non-standard and larger pack sizes to the market to distinguish products. This is a public health concern given evidence that larger pack sizes are linked to increased smoking, and could undermine existing tobacco control success.

Evidence shows that larger pack sizes are linked to increased smoking.

In a recent Addiction Opinion and Debate paper, we proposed that a cap on cigarette pack size should be introduced; a pragmatic solution would be to permit only a single pack size of 20, which is now the minimum in many countries. This approach would reduce the number of cigarettes in packs in several countries such as Australia – where packs up to sizes of 50 are available – and prevent larger sizes being introduced elsewhere.

Capping cigarette pack size therefore has the potential to both reduce smoking and prevent increased smoking. While the health benefits of reducing smoking alone are small, it may have important indirect effects on health through its role in facilitating quitting. Those smoking fewer cigarettes per day are more likely to attempt to quit and succeed in doing so. Trials of smoking-reduction interventions have also found that these can lead to increased quitting when combined with nicotine replacement therapy.

Our Opinion and Debate paper drew on evidence from a range of sources including industry documents and analyses, population surveys, intervention trials and Mendelian randomization analyses. Together these suggest that consumption increases with larger pack size, and cessation increases with reduced consumption. However, direct experimental evidence is not currently available to determine whether pack size influences the amount of tobacco consumed, or whether the association is due to other factors.

People who want to quit may be using smaller packs as a method of self-control, and smokers who successfully cut down and later quit may be more motivated to do so. Cost is also an important factor and larger packs may be linked to increased smoking because they have a lower cost per cigarette. Further research is needed to determine whether the associations between pack size, smoking and cessation are causal to estimate the impact of policies to cap cigarette pack size.

Commentaries on our Opinion and Debate paper, published in the May 2020 Issue of Addiction highlight the need to understand the mechanisms for the associations observed between pack size and smoking in order to identify the optimal cigarette pack size. Although mandating packs of 20 is a pragmatic approach, pack size regulation needs to achieve a compromise between tobacco affordability and smokers’ self-regulation. Nevertheless, the policy debate should start now to address this neglected aspect of tobacco control.

To find out more visit the Behaviour Change by Design website or follow us on Twitter @BehavChangeDsgn @BristolTARG

 

This blog post is reposted from the TARG blog.

We should be cautious about associations of patient characteristics with COVID-19 outcomes that are identified in hospitalised patients.

Gareth J Griffith, Gibran Hemani, Annie Herbert, Giulia Mancano, Tim Morris, Lindsey Pike, Gemma C Sharp, Matt Tudball, Kate Tilling and Jonathan A C Sterne, together with the authors of a preprint on collider bias in COVID-19 studies.

All authors are members of the MRC Integrative Epidemiology Unit at the University of Bristol. Jonathan Sterne is Director of Health Data Research UK South West

Among successful actors, being physically attractive is inversely related to being a good actor. Among American college students, being academically gifted is inversely related to being good at sport.

Among people who have had a heart attack, smokers have better subsequent health than non-smokers. And among low birthweight infants, those whose mothers smoked during pregnancy are less likely to die than those whose mothers did not smoke.

These relationships are not likely to reflect cause and effect in the general population: smoking during pregnancy does not improve the health of low birthweight infants. Instead, they arise from a phenomenon called ‘selection bias’, or ‘collider bias’.

Understanding selection bias

Selection bias occurs when two characteristics influence whether a person is included in a group for which we analyse data. Suppose that two characteristics (for example, physical attractiveness and acting talent) are unrelated in the population but that each causes selection into the group (for example, people who have a successful Hollywood acting career). Among individuals with a successful acting career we will usually find that physical attractiveness will be negatively associated with acting talent: individuals who are more physically attractive will be less talented actors (Figure 1). Selection bias arises if we try to infer a cause-effect relationship between these two characteristics in the selected group. The term ‘collider bias’ refers to the two arrows indicating cause and effect that ‘collide’ at the effect (being a successful actor).

Figure 1: Selection effects exerted on successful Hollywood actors. Green boxes highlight characteristics that influence selection. Yellow boxes indicate the variable selected upon. Arrows indicate causal relationships: the dotted line indicates a non-causal induced relationship that arises because of selection bias.

Figure 2 below explains this phenomenon. Each point represents a hypothetical person, with their level of physical attractiveness plotted against their level of acting talent. In the general population (all data points) an individual’s attractiveness tells us nothing about their acting ability – the two characteristics are unrelated. The red data points represent successful Hollywood actors, who tend to be more physically attractive and to be more talented actors. The blue data points represent other people in the population. Among successful actors the two characteristics are strongly negatively associated (green line), solely because of the selection process. The direction of the bias (whether it is towards a positive or negative association) depends on the direction of the selection processes. If they act in the same direction (both positive or both negative) the bias will usually be towards a negative association. If they act in opposite directions the bias will usually be towards a positive association.

Figure 2:  The effect of sample selection on the relationship between attractiveness and acting talent. The green line depicts the negative association seen in successful actors.

 

Why is selection bias important for COVID-19 research?

In health research, selection processes may be less well understood, and we are often unable to observe the unselected group. For example, many studies of COVID-19 have been restricted to hospitalised patients, because it was not possible to identify all symptomatic patients, and testing was not widely available in the early phase of the pandemic. Selection bias can seriously distort relationships of risk factors for hospitalisation with COVID-19 outcomes such as requiring invasive ventilation, or mortality.

Figure 3 shows how selection bias can distort risk factor associations in hospitalised patients. We want to know the causal effect of smoking on risk of death due to COVID-19, and the data available to us is on patients hospitalised with COVID-19. Associations between all pairs of factors that influence hospitalisation will be distorted in hospitalised patients. For example, if smoking and frailty each make an individual more likely to be hospitalised with COVID-19 (either because they influence infection with SARS-CoV-2 or because they influence COVID-19 disease severity), then their association in hospitalised patients will usually be more negative than in the whole population. Unless we control for all causes of hospitalisation, our estimate of the effect of any individual risk factor on COVID-19 mortality will be biased. For example, it would be unsurprising that within hospitalised patients with COVID-19 we observe that smokers have better health than non-smokers because they are likely to be younger and less frail, and therefore less likely to die after hospitalisation. But that finding may not reflect a protective effect of smoking on COVID-19 mortality in the whole population.

Figure 3: Selection effects on hospitalisation with COVID-19. Box colours are as in Figure 1. Blue boxes represent outcomes. Arrows indicate causal relationships, the dotted line indicates a non-causal induced relationship that arises because of selection bias.

 

Selection bias may also be a problem in studies based on data from participants who volunteer to download and use COVID-19 symptom reporting apps. People with COVID-19 symptoms are more likely to use the app, and so are people with other characteristics (younger people, people who own a smartphone, and those to whom the app is promoted on social media). Risk factor associations within app users may therefore not generalise to the wider population.

What can be done?

Findings from COVID-19 studies conducted in selected groups should be interpreted with great caution unless selection bias has been explicitly addressed. Two ways to do so are readily available. The preferred approach uses representative data collection for the whole population to weight the sample and adjust for the selection bias.  In absence of data on the whole population, researchers should conduct sensitivity analyses that adjust their findings based on a range of assumptions about the selection effects. A series of resources providing further reading, and tools allowing researchers to investigate plausible selection effects are provided below.

For further information please contact Gareth Griffith (g.griffith@bristol.ac.uk) or Jonathan Sterne (jonathan.sterne@bristol.ac.uk).

Further reading and selection tools:

Dahabreh IJ and Kent DM. Index Event Bias as an Explanation for the Paradoxes of Recurrence Risk Research. JAMA 2011; 305(8): 822-823.

Griffith, Gareth, Tim M. Morris, Matt Tudball, Annie Herbert, Giulia Mancano, Lindsey Pike, Gemma C. Sharp, Jonathan Sterne, Tom M. Palmer, George Davey Smith, Kate Tilling, Luisa Zuccolo, Neil M. Davies, and Gibran Hemani. Collider Bias undermines our understanding of COVID-19 disease risk and severity. Interactive App 2020 http://apps.mrcieu.ac.uk/ascrtain/

Groenwold, RH, Palmer TM and Tilling K. Conditioning on a mediator to adjust for unmeasured confounding OSF Preprint 2020: https://osf.io/vrcuf/

Hernán MA, Hernández-Díaz S and Robins JM. A structural approach to selection bias. Epidemiology 2004; 15: 615-625.

Munafo MR, Tilling K, Taylor AE, Evans DM and Davey Smith G. Collider Scope: When Selection Bias Can Substantially Influence Observed Associations. International Journal of Epidemiology 2018; 47: 226-35.

Luque-Fernandez MA, Schomaker M, Redondo-Sanchez D, Sanchez Perez MJ, Vaidya A and Schnitzer ME. Educational Note: Paradoxical collider effect in the analysis of non-communicable disease epidemiological data: a reproducible illustration and web application International Journal of Epidemiology 2019; 48: 640-653. Interactive App: https://watzilei.com/shiny/collider/

Smith LH and VanderWeele TJ. Bounding bias due to selection. Epidemiology 2019; 30: 509-516. Interactive App: https://selection-bias.herokuapp.com

 

COVID-19 and community support: Mapping unmet support needs across Wales

Dr Oliver Davis, Nina Di Cara, and the project team 

Follow OliverValerioAlastairNina, Chris Benjamin and Public Health Wales Research & Evaluation on Twitter 

Since the pandemic started, communities have been mobilising to help each other; from shopping for elderly neighbours, to offering to offering a friendly face or other support.  Mutual aid networks have sprung up all over the country, and neighbours who hadn’t previously spoken have been introduced to each other via streetlevel WhatsApp groups. But the degree to which offers of help are matching up with the need for help has been unknown, and this poses a problem for organisations who need to make decisions about where they should target limited resources.   

Screenshot from the https://covidresponsemap.wales/ site.

Ensuring support is available where needed 

Community support can offer a protective factor against adverse events. Some areas are more vulnerable than others, but knowing which areas are most likely to have a mismatch between support needed and support offered is difficult. To address this issue, a collaboration between the Public Health Wales Research & Evaluation Division and the Dynamic Genetics lab, part of the MRC Integrative Epidemiology Unit at the University of Bristol and supported by the Alan Turing Institute, has mapped these support offers and needs 

Using data from Wales Council for Voluntary Action, COVID-19 Mutual Aid, Welsh Government Statistics and Research, the Office for National Statistics, and social media the project team have created a live map that highlights the areas where further support for communities may be needed. It shows data on support factors, such as number of registered volunteers and population density, against risks, such as demographics, levels of deprivation, and internet access. It aims to inform the responses of national and local government, as well as support providers in Wales. 

The site also provides the links to local community groups identified helping to raise awareness of the support available locally. 

This map is part of an effort to better understand which communities have better community cohesion and organisation. We are keen to find out your views on how this can be more useful, or other community mobilisation data sources which could be included. Please contact Oliver or Nina with your comments: 

Dr Oliver Davis: oliver.davis@bristol.ac.uk  

Nina Di Cara nina.dicara@bristol.ac.uk 

 

Further information 

 

Are teachers at high risk of death from Covid19?

Sarah Lewis, George Davey Smith and Marcus Munafo

Follow Sarah, George and Marcus on Twitter

Due to the SARS-CoV-2 pandemic schools across the United Kingdom were closed to all but a small minority of pupils (children of keyworkers and vulnerable children) on the 20th March 2020, with some schools reporting as few as 5 pupils currently attending. The UK government have now issued guidance that primary schools in England should start to accept pupils back from the 1st June 2020 with a staggered return, starting with reception, year 1 and year 6.

Concern from teachers’ unions

This has prompted understandable concern from the  teachers’ unions, and on the 13th May, nine unions which represent teachers and education professionals signed a joint statement calling on the government to postpone reopening school on the 1st June, “We all want schools to re-open, but that should only happen when it is safe to do so. The government is showing a lack of understanding about the dangers of the spread of coronavirus within schools, and outwards from schools to parents, sibling and relatives, and to the wider community.” At the same time, others have suggested that the harms to many children due to neglect, abuse and missed educational opportunity arising from school closures outweigh the small increased risk to children, teachers and other adults of catching the virus.

What risk does Covid19 pose to children?

Weighing up the risks to children and teachers

So what do we know about the risk to children and to teachers? We know that children are about half as likely to catch the virus from an infected person as adults, and  if they do catch the virus they  are likely to have only mild symptoms.  The current evidence, although inconclusive, also suggests that they may be less likely to transmit the virus than adults.  However, teachers have rightly pointed out that there is a risk of transmission between the teachers themselves and between parents and teachers.

The first death from COVID-19 in England was recorded at the beginning of March 2020 and by the 8th May 2020 39,071 deaths involving COVID-19 had been reported in England and Wales. Just three of these deaths were among children aged under 15 years and  only a small proportion of the deaths (4416 individuals, 11.3%) were among working aged people.  Even among this age group risk is not uniform; it increases sharply with age from 2.6 in 100,000 for 25-44 years olds with a ten fold increase to 26 in  100,000 individuals for those aged 45-64.

Risks to teachers compared to other occupations

In addition, each underlying health condition increases the risk of dying from COVID-19, with those having at least 1 underlying health problem making up most cases.   The Office for National Statistics in the UK have published age standardised deaths by occupation for all deaths involving COVID-19 up to the 20th April 2020. Most of the people dying by this date would have been infected at the peak of the pandemic in the UK  prior to the lockdown period. They found that during this period there were 2494 deaths involving Covid-19 in the working age population. The mortality rate for Covid-19 during this period was 9.9 (95% confidence intervals 9.4-10.4) per 100,000 males and 5.2 (95%CI 4.9-5.6) per 100,000 females, with Covid-19 involved in around 1 in 4 and 1 in 5 of all deaths among males and females respectively.

Amongst teaching and education professionals (which includes school teachers, university lecturers and other education professionals) a total of 47 deaths (involving Covid-19) were recorded, equating to mortality rates of 6.7 (95%CI 4.1-10.3) per 100,000 among males and 3.3 (95%CI 2.0-4.9) per 100,000 among females, which was very similar to the rates of 5.6 (95%CI 4.6-6.6) per 100,000 among males and 4.2(95%CI 3.3-5.2) per 100,000 females for all professionals. The mortality figures for all education professionals includes 7 out of 437000 (or 1.6 per 100,000 teachers) primary and nursery school teachers and 17 out of 395000 (or 4.3 per 100,000 teachers) secondary school teachers.  A further 20 deaths occurred amongst childcare workers giving a mortality rate amongst this group of 3.4 (95%CI=2.0-5.5) per 100,000 females (males were highly underrepresented in this group), this is in contrast to rates of 6.5 (95%CI=4.9-9.1) for female sales assistants and 12.7(95%CI= 9.8-16.2) for female care home workers.

Covid-19 risk does not appear greater for teachers than other working age individuals

In summary, based on current evidence the risk to teachers and childcare workers within the UK from Covid-19 does not appear to be any greater than for any other group of working age individuals. However, perceptions of elevated risk may have occurred, prompting some to ask “Why are so many teachers dying?” due to the way this issue is portrayed in the media with headlines such as “Revealed: At least 26 teachers have died from Covid-19” currently on the https://www.tes.com website. This kind of reporting, along with the inability of the government to communicate the substantial differences in risk between different population groups – in particular according to age – has caused understandable anxiety among teachers. Whilst, some teachers may not be prepared to accept any level of risk of becoming infected with the virus whilst at work, others may be reassured that the risk to them is small, particularly given that we all accept some level of risk in our lives, a value that can never be zero.

Likely impact on transmission in the community is unclear

As the majority of parents or guardians of school aged children will be in the 25-45 age range, the risk to them  is also likely to be small. Questions remain however around the effect of school openings on transmission in the community and the associated risk. This will be affected by many factors including the existing infection levels in the community, the extent to which pupils, parents and teachers are mixing outside of school (and at the school gate) and mixing between individuals of different age groups. This is the primary consideration of the government Scientific Advisory Group for Emergencies (SAGE) who are using modelling based on a series of assumptions to determine the effect of school openings on R0.

 

Sarah Lewis is a Senior Lecturer in Genetic Epidemiology in the department of Population Health Sciences, and is an affiliated member of the MRC Integrative Epidemiology Unit (IEU), University of Bristol

George Davey Smith is a Professor of Clinical Epidemiology, and director of the MRC IEU, University of Bristol

Marcus Munafo is a Professor of Biological Psychology, in the School of Psychology Science and leads the Causes, Consequences and Modification of Health Behaviours programme of research in the IEU, University of Bristol.

 

Collider bias: why it’s difficult to find risk factors or effective medications for COVID-19 infection and severity

Dr Gemma Sharp and Dr Tim Morris

Follow Gemma and Tim on twitter

 

The COVID-19 pandemic is proving to be a period of great uncertainty. Will we get it? If we get it, will we show symptoms? Will we have to go to hospital? Will we be ok? Have we already had it?

These questions are difficult to answer because, currently, not much is known about who is more at risk of being infected by coronavirus, and who is more at risk of being seriously ill once infected.

Researchers, private companies and government health organisations are all generating data to help shed light on the factors linked to COVID-19 infection and severity. You might have seen or heard about some of these attempts, like the COVID-19 Symptom Tracker app developed by scientists at King’s College London, and the additional questions being sent to people participating in some of the UK’s biggest and most famous health studies, like UK Biobank and the Avon Longitudinal Study of Parents and Children (ALSPAC).

These valuable efforts to gather more data will be vital in providing scientific evidence to support new public health policies, including changes to the lockdown strategy. However, it’s important to realise that data gathered in this way is ‘observational’, meaning that study participants provide their data through medical records or questionnaires but no experiment (such as comparing different treatments) is performed on them. The huge potential impact of COVID-19 data collection efforts makes it even more important to be aware of the difficulties of using observational data.

Image by Engin Akyurt from Pixabay

Correlation does not equal causation (the reason observational epidemiology is hard)

These issues boil down to one main problem with observational data: that it is difficult to tease apart correlation from causation.

There are lots of factors that correlate but clearly do not actually have any causal effect on each other. Just because, on average, people who engage in a particular behaviour (like taking certain medications) might have a higher rate of infection or severe COVID-19 illness, it doesn’t necessarily mean that this behaviour causes the disease. If the link is not causal, then changing the behaviour (for example, changing medications) would not change a person’s risk of disease. This means that a change in behaviour would provide no benefit, and possibly even harm, to their health.

This illustrates why it’s so important to be sure that we’re drawing the right conclusions from observational data on COVID-19; because if we don’t, public health policy decisions made with the best intentions could negatively impact population health.

Why COVID-19 research participants are not like everyone else

One particular issue with most of the COVID-19 data collected so far is that the people who have contributed data are not a randomly drawn or representative sample of the broader general population.

Only a small percentage of the population are being tested for COVID-19, so if research aims to find factors associated with having a positive or negative test, the sample is very small and not likely to be representative of everyone else. In the UK, people getting the test are likely to be hospital patients who are showing severe symptoms, or healthcare or other key workers who are at high risk of infection and severe illness due to being exposed to large amounts of the virus. These groups will be heavily over-represented in COVID-19 research, and many infected people with no or mild symptoms (who aren’t being tested) will be missed.

Aside from using swab tests, researchers can also identify people who are very likely to have been infected by asking about classic symptoms like a persistent dry cough and a fever. However, we have to consider that people who take part in these sorts of studies are also not necessarily representative of everyone else. For example, they are well enough to fill in a symptom questionnaire. They also probably use social media, where they likely found out about the study. They almost certainly own a smartphone as they were able to download the COVID-19 Symptom Tracker app, and they are probably at least somewhat interested in their health and/or in scientific research.

Why should we care about representativeness?

The fact that people participating in COVID-19 research are not representative of the whole population leads to two problems, one well-known and one less well-known.

Firstly, as often acknowledged by researchers, research findings might not be generalisable to everyone in the population. Correlations or causal associations between COVID-19 and the characteristics or behaviours of research participants might not exist amongst the (many more) people who didn’t take part in the research, but only in the sub-group who participated. So the findings might not translate to the general population: telling everyone to switch up their medications to avoid infection may only work for some people who are like those studied.

But there is a second problem, called ‘collider bias’ (sometimes also referred to using other names such as selection bias or sampling bias), that is less well understood and more difficult to grasp. Collider bias can distort findings so that certain factors appear related even when there is no relationship in the wider population. In the case of COVID-19 research, relationships between risk factors and infection (or severity of infection) can appear related when no causal effect exists, even within the sample of research participants.

As an abstract example, consider a private school where pupils are admitted only if they have either a sports scholarship or an academic scholarship. If a pupil at this school is not good at sports, we can deduce that they must be good at academic work. This correlation between being poor at sports but being good academically doesn’t exist in the real world outside of this school, but in the sample of school pupils, it appears. And so, with COVID-19 research, in the sample of people included in a COVID-19 dataset (e.g. people who have had a COVID-19 test), two factors that influence inclusion (e.g. having COVID-19 symptoms that were severe enough to warrant hospitalisation, and taking medications for a health condition that puts you at high risk of dying from COVID-19) would appear to be associated, even when they are not. That is, to be in the COVID-19 dataset (to be tested), people are likely to have had either more severe symptoms or to be on medication. The erroneous conclusion would follow that changing one factor (e.g. changing or stopping medications) would affect the other (i.e. lower the severity of COVID-19). Because symptom severity is related to risk of death, stopping medication would appear to reduce the chance of death. As such, any resulting changes to clinical practice would be ineffective or even harmful.

Policymaking is a complex process at the best of times, involving balancing evidence from research, practice, and personal experience with other constraints and drivers, such as resource pressures, politics, and values. Add into that the challenge of making critical decisions with incomplete information under intense time pressure, and the need for good quality evidence becomes even more acute. The expertise of statisticians, who can double check analyses and ensure that conclusions are as robust as possible, should be a central part of the decision making process at this time – and especially to make sure that erroneous conclusions arrived at as a result of collider bias do not translate into harmful practice for people with COVID-19.

 

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The main aim of this blog post was to highlight the issue of collider bias, which is notoriously tricky to grasp. We hope we’ve done this but would be interested in your comments.

For those looking for more information, read on to discover some of the statistical methods that can be used to address collider bias….

Now we know collider bias is a problem: how do we fix it?

It is important to consider the intricacies of observational data and highlight the very real problems that can arise from opportunistically collected data.  However, this needs to be balanced against the fact that we are in the middle of a pandemic, that important decisions need to be made quickly, and this data is all we have to guide decisions. So what can we do?

There are a few strategies, developed by statisticians and other researchers in multiple fields, that should be considered when conducting COVID-19 research:

  •       Estimate the extent of the collider bias:

o  Think about the profile of people in COVID-19 samples – are they older/younger or more/less healthy than individuals in the general population?

o Are there any unexpected correlations in the sample that ring alarm bells?

  • Try to balance out the analysis by ‘weighting’ individuals, so that people from under-represented groups count more than people from over-represented groups.
  • Carry out additional analysis, known as ‘sensitivity analysis’, to assess the extent to which plausible patterns of sample selection could alter measured associations.

For those who would like to read even more, here’s a pre print on collider bias published by our team:

Gareth GriffithTim T MorrisMatt TudballAnnie HerbertGiulia MancanoLindsey PikeGemma C SharpTom M PalmerGeorge Davey SmithKate TillingLuisa ZuccoloNeil M DaviesGibran Hemani

 

New evidence on risks of low-level alcohol use in pregnancy

Dr Kayleigh Easey (@KayEasey), from the Bristol Medical School’s MRC Integrative Epidemiology Unit at the University of Bristol, takes a look at a recent research investigating effects of drinking in pregnancy and child mental health.

Whilst it’s generally known that heavy alcohol use during pregnancy can cause physical and cognitive impairments in offspring, there has been relatively limited evidence about the effects of low to moderate alcohol use. As such there have been conflicting conclusions about the potential harm of drinking in pregnancy, and debate around official guidance.

Alcohol use in pregnancy is still common with a recent meta-analysis showing over 40% of women within the UK to have drank some alcohol whilst pregnant. In 2016 the Department of Health updated their guidance advising abstinence from alcohol throughout pregnancy. This was in contrast to their previous advice of abstaining from alcohol in the first three months, but that 1-2 units of alcohol per week were not likely to cause harm. The updated guidance reflected a precautionary approach based on researcher’s advice of ‘absence of evidence not being evidence of absence’, due to the challenges faced in this area of study.

Certainly it has been challenging for researchers to determine any causal effect of alcohol use in pregnancy, particularly as the existing observational studies do not show evidence of causality. As such, caution over interpretation of results is needed given the sensitivity of alcohol as a risk factor and traditional attitudes towards low-level drinking.

Our new research sought to add to the limited body of evidence investigating causal effects, specifically on how low to moderate alcohol use could influence offspring mental health. We used data from a longitudinal birth cohort (the Avon Longitudinal Study of Parents and Children) which has followed pregnant mothers, their partners and their offspring since the 1990s, to investigate whether the frequency mothers and their partners drank alcohol during pregnancy was associated with offspring depression at age 18. We also included partner’s alcohol use in pregnancy (which is unlikely to have a direct biological effect on the developing fetus) and were able to examine if associations were likely to be causal, or due to shared confounding factors between parents such as socio-demographic factors.

We found that children whose mothers drank any alcohol at 18 weeks pregnancy may have up to a 17% higher risk of depression at age 18 compared to those mothers who did not drink alcohol. What was really interesting here is that we also investigated paternal alcohol use during pregnancy and did not find a similar association. This suggests that the associations seen with maternal drinking may be causal, rather than due to confounding by other factors (which might be expected to be similar between mothers and their partners). Many of the indirect factors that could explain the maternal effects are shared between mothers and partners (such as socio-demographic factors); despite this, we only found associations for mothers drinking.

These findings suggest evidence of a likely causal effect from alcohol consumption during pregnancy, and therefore evidence to support the updated government advice that the safest approach for alcohol use during pregnancy is for abstinence. This adds to other limited research on the effects of low level alcohol use in pregnancy. Whilst further research is needed, women can use this information to further inform their choices and help avoid risk from alcohol use both during pregnancy and as a precautionary measure when trying to conceive, as supported by the #Drymester campaign.

Our study highlights also the importance of including partner behaviours during pregnancy to aid in identifying causal relationships with offspring outcomes, and also because these may be important in their own right. I am currently working within the EPoCH project (Exploring Parental Influences on Childhood Health) which investigates whether and how both maternal and paternal behaviours might impact childhood health. In the meantime, it may be time for a further public health promotion highlighting that an alcohol-free pregnancy really is safer for children’s health.

 

This post was originally published on the Alcohol Policy blog.

What can genetics tell us about how sleep affects our health?

Deborah Lawlor, Professor of Epidemiology, Emma Anderson, MRC Research Fellow, Marcus Munafò, Professor of Experimental Psychology, Mark Gibson, PhD student, Rebecca Richmond, Vice Chancellor’s Research Fellow

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Association is not causation – are we fooled (confounded) when we see associations between sleep problems and disease?

Sleep is important for health. Observational studies show that people who report having sleep problems are more likely to be overweight, and have more health problems including heart disease, some cancers and mental health problems.

A major problem with conventional observational studies is that we cannot tell whether these associations are causal; does being overweight cause sleep problems, or do sleep problems cause people to become overweight? Alternatively, factors that influence how we sleep may also influence our health. For example, smoking might cause sleep problems as well as heart disease and so we are fooled (confounded) into thinking sleep problems cause heart disease when it is really all explained by smoking. In the green paper Advancing our Health: Prevention in the 2020s, the UK Government acknowledged that sleep has had little attention in policy, and that causality between sleep and health is likely to run in both directions.

But, how can we determine the direction of causality for sure? And, how do we make sure we are results are not confounded?

Randomly allocated genetic variation

Our genes are randomly allocated to us from our parents when we are conceived. They do not change across our lifespan, and cannot be changed by smoking, overweight or ill health.

Here at the MRC Integrative Epidemiology Unit we have developed a research method called Mendelian randomization, which uses this family-level random allocation of genes to explore causal effects. To find out more about Mendelian randomization take a look at this primer from the Director of the Unit (Prof George Davey Smith).

In the last two years, we and colleagues from the Universities of Manchester, Exeter and Harvard have identified large numbers of genetic variants that relate to different sleep characteristics. These include:

  • Insomnia symptoms
  • How long, on average, someone sleeps each night
  • Chronotype (whether someone is an ‘early bird’ or ‘lark’ and prefers mornings, or a ‘night owl’ and prefers evenings). Chronotype is thought to reflect variation in our body clock (known as circadian rhythms).

We can use these genetic variants in Mendelian randomization studies to get a better understanding of whether sleep characteristics affect health and disease.

What we did

In our initial studies we used Mendelian randomization to explore the effects of sleep duration, insomnia and chronotype on body mass index, coronary heart disease, mental health problems, Alzheimer’s disease, and breast cancer. We analysed whether the genetic traits that are related to sleep characteristics – rather than the sleep characteristics themselves – are associated with the health outcomes. We combined those results with the effect of the genetic variants on sleep traits which allows us to estimate a causal effect. Using genetic variants rather than participants’ reports of their sleep characteristics makes us much more certain that the effects we identify are not due to confounding or reverse causation.

Are you a night owl or a lark?

What we found

Our results show a mixed picture; different sleep characteristics have varying effects on a range of health outcomes.

What does this mean?

Having better research evidence about the effects of sleep traits on different health outcomes means that we can give better advice to people at risk of specific health problems. For example, developing effective programmes to alleviate insomnia may prevent coronary heart disease and depression in those at risk. It can also help reduce worry about sleep and health, by demonstrating that some associations that have been found in previous studies are not likely to reflect causality.

If you are worried about your own sleep, the NHS has some useful guidance and signposting to further support.

Want to find out more?

Contact the researchers

Deborah A Lawlor mailto:d.a.lawlor@bristol.ac.uk

Further reading

This research has been published in the following open access research papers:

Genome-wide association analyses of chronotype in 697,828 individuals provides insights into circadian rhythms. Nature Comms (2019) https://www.nature.com/articles/s41467-018-08259-7

Biological and clinical insights from genetics of insomnia symptoms.  Nature Gen. (2019) https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6415688/

Genome-wide association study identifies genetic loci for self-reported habitual sleep duration supported by accelerometer-derived estimates. Nature Comms. (2019) https://www.nature.com/articles/s41467-019-08917-4

Investigating causal relations between sleep traits and risk of breast cancer in women: mendelian randomisation study. BMJ (2019) https://www.bmj.com/content/365/bmj.l2327

Is disrupted sleep a risk factor for Alzheimer’s disease? Evidence from a two-sample Mendelian randomization analysis. https://www.biorxiv.org/content/10.1101/609834v1 (open access pre-print)

Evidence for Genetic Correlations and Bidirectional, Causal Effects Between Smoking and Sleep Behaviors. Nicotine and Tobacco (2018) https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6528151/

Do development indicators underlie global variation in the number of young people injecting drugs?

Dr Lindsey Hines, Sir Henry Wellcome Postdoctoral Fellow in The Centre for Academic Mental Health & the Integrative Epidemiology Unit, University of Bristol

Dr Adam Trickey, Senior Research Associate in Population Health Sciences, University of Bristol

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Injecting drug use is a global issue: around the world an estimated 15.6 million people inject psychoactive drugs. People who inject drugs tend to begin doing so in adolescence, and countries that have larger numbers of adolescents who inject drugs may be at risk of emerging epidemics of blood borne viruses unless they take urgent action. We mapped the global differences in the proportion of adolescents who inject drugs, but found that we may be missing the vital data we need to protect the lives of vulnerable young people. If we want to prevent HIV, hepatitis C, and overdose from sweeping through a new generation of adolescents we urgently need many countries to scale up harm reduction interventions, and to collect accurate which can inform public health and policy.

People who inject drugs are engaging in a behaviour that can expose them to multiple health risks such as addiction, blood-borne viruses, and overdose, and are often stigmatised. New generations of young people are still starting to inject drugs, and young people who inject drugs are often part of other vulnerable groups.

Much of the research into the causes of injecting drug use focuses on individual factors, but we wanted to explore the effect of global development on youth injecting. A recent systematic review showed wide country-level variation in the number of young people who comprise the population of people who inject drugs. By considering variation in countries, we hoped to be able to inform prevention and intervention efforts.

It’s important to note that effective interventions can reduce the harms of injecting drug use. Harm reduction programmes provide clean needles and syringes to reduce transmission of blood borne viruses. Opiate substitution therapy seeks to tackle the physical dependence on opiates that maintains injecting behaviour and has been shown to improve health outcomes.

What we did

Through a global systematic review and meta-analysis we aimed to find data on injecting drug use in published studies, public health and policy documents from every country. We used these data to estimate the global percentage of people who inject drugs that are aged 15-25 years old, and also estimated this for each region and country. We wanted to understand what might underlie variation in the number of young people in populations of people who inject drugs, and so we used data from the World Bank to identify markers of a country’s wealth, equality, and development.

What we found

Our study estimated that, globally, around a quarter of people who inject drugs are adolescents and young adults. Applied to the global population, we can estimate approximately 3·9 million young people inject drugs. As a global average, people start injecting drugs at 23 years old.

Estimated percentage of young people amongst those who inject drugs in each country

We found huge variation in the percentage of young people in each country’s population of people who inject drugs. Regionally, Eastern Europe had the highest proportion of young people amongst their populations who inject drugs, and the Middle Eastern and North African region had the lowest. In both Russia and the Philippines, over 50% of the people who inject drugs were aged 25 or under, and the average age of the populations of people who inject drugs was amongst the lowest observed.

Average age of the population of people who inject drugs in each country

In relation to global development indicators, people who inject drugs were younger in countries with lower wealth (indicated through Gross Domestic Product per capita) had been injecting drugs for a shorter time period. In rapidly urbanising countries (indicated through urbanisation growth rate) people were likely to start injecting drugs at later ages than people in countries with a slower current rate of urbanisation. We didn’t find any relationships between the age of people who inject drugs and a country’s youth unemployment, economic equality, or level provision of opiate substitution therapy.

However, many countries were missing data on injecting age and behaviours, or injecting drug use in general, which could affect these results.

What this means

1. The epidemic of injecting drug use is being maintained over time.

A large percentage of people who inject drugs are adolescents, meaning that a new generation are being exposed to the risks of injecting – and we found that this risk was especially high in less wealthy countries.

2. We need to scale up access to harm reduction interventions

There are highly punitive policies towards drug use in the countries with the largest numbers of young people in their populations of people who inject drugs. Since 2016, thousands of people who use drugs in the Philippines have died at the hands of the police. In contrast, Portugal has adopted a public health approach to drug use and addiction for decades, taking the radical step of taking people caught with drugs or personal use into addiction services rather than prisons. The rate of drug-related deaths and HIV infections in Portugal has since plummeted, as has the overall rate of drug use amongst young people: our data show that Portugal has a high average age for its population of people who inject drugs. If we do not want HIV, hepatitis C, and drug overdoses to sweep through a new generation of adolescents, we urgently need to see more countries adopting the approach pioneered by Portugal, and scaling up access to harm reduction interventions to the levels recommended by the WHO.

3. We need to think about population health, and especially mental health, alongside urban development.

Global development appears to be linked to injecting drug use, and the results suggest that countries with higher urbanisation growth are seeing new, older populations beginning to inject drugs. It may be that changes in environment are providing opportunities for injecting drug use that people hadn’t previously had. It’s estimated that almost 70% of the global population will live in urban areas by 2050, with most of this growth driven by low and middle-income countries.

4. We need to collect accurate data

Despite the health risks of injecting drug use, and the urgent need to reduce risks for new generations, our study has revealed a paucity of data monitoring this behaviour. Most concerning, we know the least about youth injecting drug use in low- and middle-income countries: areas likely to have the highest numbers of young people in their populations of people who inject drugs. Due to the stigma and the illicit nature of injecting drug use it is often under-studied, but by failing to collect accurate data to inform public health and policy we are risking the lives of vulnerable young people.

Contact the researchers

Lindsey.hines@bristol.ac.uk

Lindsey is funded by the Wellcome Trust.

Beyond question: a collaboration between EPoCH and artist Olga Trevisan

Back in May, IEU researcher Dr Gemma Sharp took part in Creative Reactions, an initiative that pairs scientists with artists to create artwork based on their academic research. With 50 artists and 50 scientists collaborating on works from sculptures and wood carvings to canvas, digital and performance art, the 2019 exhibition ran across two venues in Bristol.

Gemma was paired with Olga Trevisan, an artist based in Venice, Italy. They had conversations over Skype where they spoke about their work and formed some initial ideas about how they could combine their interests in a new way while remaining coherent to their own practices. Reflecting on the collaboration, Olga said, “I love how curious you can be of a subject you haven’t considered before. I believe collaboration helps to open your own mind.”

Based on some of the work around EPoCH, Olga created a piece called Beyond Question, which comments on the complexities of scientific data collection, bias and interpretation.

It poses questions around the pervasive assumption that pregnant women are more responsible for the (ill) health of their unborn children than their male partners are. Gemma and colleagues have argued that such assumptions drive the research agenda and the public perception of parental roles, by shaping which research questions get asked, which data are collected, and the quality of the scientific ‘answer’.

Photo credit: Olga Trevisan

Beyond Question was presented in two phases at two separate exhibitions: during the first phase, people were invited to answer questions with a simple Yes or No using a stylus; leaving no marks but only invisible, anonymous traces on the surface below. Answers will reflect the real assumptions, beliefs and attitudes of the respondent, but perhaps also, despite anonymity, their eagerness to ‘please’ the questioners, to give the ‘right’ answer, and to mask their true responses to paint themselves in the ‘best’ light.

In the second phase, the questions were removed and the answer traces were left alone to carry their own meaning; free to be combined with the attitudes, beliefs and assumptions of the viewer and to be interpreted and judged in perhaps an entirely different way.

Photo credit: Olga Trevisan

The questions posed were:

  • “Do you think a mother’s lifestyle around the time of pregnancy could be bad for her baby’s health?”
  • “Do you think a father’s lifestyle around the time of pregnancy could be bad for his baby’s health?”
  • “Before her baby is born, a pregnant mother shouldn’t be allowed to do unhealthy things, like smoke or drink alcohol. Do you agree or disagree?”
  • “Before his baby is born, a father shouldn’t be allowed to do unhealthy things, like smoke or drink alcohol. Do you agree or disagree?”

Find out more

Further info about Creative Reactions Bristol is available on their facebook page, or contact Matthew Lee on matthew.lee@bristol.ac.uk 

This blog post was originally posted on the EPOCH blog.

Using evidence to advise public health decision makers: an insider’s view

This blog post reviews a recent seminar hosted by the MRC IEU, PolicyBristol and the Bristol Population Health Science Institute.

Public health is one of the most contested policy areas. It brings together ethical and political issues and evidence on what works, and affects us all as citizens.

Researchers produce evidence and decision-makers receive advice – but how does evidence become advice and who are the players who take research findings and present advice to politicians and budget-holders?

We were pleased to welcome a diverse audience of around 75 multidisciplinary academics, policymakers and practitioners to hear our seminar speakers give a range of insider perspectives on linking academic research with national and local decisions on what to choose, fund and implement.

In this blog post we summarise the seminar, including links to the slides and event recording.

Seminar audience
Seminar attendees in the Coutts lecture theatre. Image credit: Julio Hermosilla Elgueta

Chair David Buck from The King’s Fund opened the event, highlighting the importance of conversations between different sectors of the evidence landscape, and of local decision-making in this context.

‘The art of giving advice’

The session was kicked off by Richard Gleave, Deputy Chief Executive, Public Health England, who is also undertaking a PhD on how evidence is used in public health policy decision making.

His presentation ‘Crossing boundaries – undertaking knowledge informed public health’ set the context, observing that most academic teams – from microbiology labs to mental health researchers – aim to improve policy and practice; but ‘the art of giving advice is as important and challenging as the skill required to review the evidence’.

Richard then introduced a range of provocations and stereotypes about how the policy decision making process can be framed.

Citing Dr Kathryn Oliver, he encouraged attendees to challenge the idea that there’s an ‘evidence gap’ to be crossed, and instead focus on doing good working together to improve the public’s health.

Giving an example of the Institute for Government’s analysis of how the smoking ban was enacted, he noted the role of a small number of influential groups and individuals in securing a total ban in 2007. He encouraged actively crossing the boundaries between academia, policy and practice, and working with boundary organisations and influencers as part of this process.

‘Partnerships between science and society’

Professor Isabel Oliver gave a second national perspective.

Speaking as a research-active Director of Research, Translation and Innovation and Deputy Director of the National Infection Service, she suggested that ‘Partnerships between Science and Society’ are the key to evidence based public health.

She questioned why is it when we have such an abundance of research, we still don’t have the evidence we need? And why does it take so long to implement research findings? She argued that a key issue here is relevance; how relevant is the research being produced, especially to current policy priorities?

Isabel outlined challenges including:

  • Needing evidence quickly in response to public health emergencies, and not being able to access it, for example how to bottle-feed babies during flooding crises, or whether to close schools during flu pandemics
  • Mismatched policy and research priorities; e.g. policy needing evidence on the impact of advertising on childhood obesity, but research focusing on the genetics of obesity
  • The (unhelpful) prevalence of ‘more research needed’ as a conclusion, and knowing when the evidence is sufficient to make a decision
  • A need to develop trust between stakeholders, made more challenging by the frequency of policy colleagues moving roles.

She also questioned whether the paradigm of evidence-based medicine works for complex issues such as public health or environmental policy.

Isabel concluded with some observations; that broader and more collaborative research questions that address the real issues are needed; and collaborating with a broad range of stakeholders, including industry and finance, should not be discounted.

She finished by reiterating a call for public health advice that is relevant, and responds to a policy ‘window’ being open.

Seminar speakers L-R: Dr Olivia Maynard, Richard Gleave, Professor Isabel Oliver and Christina Gray. Image credit: Julio Hermosilla Elgueta

‘Local perspective’

Christina Gray, Director of Public Health at Bristol City Council gave the local view, providing a helpful explanation of her role and the process of decision making within a local authority.

She outlined three key principles:

  • The democratic principle; elected members are ‘the council’; officers (including her role) provide advice. Local authorities are close to their people and are publicly accountable. Their decisions are formally scrutinised and need to be justified, and resource allocation is a key – and stark – challenge, especially in the context of austerity.
  • The narrative principle: how the society that the authority represents holds multiple legitimate (and competing or conflicting) perspectives and realities, which all need to be considered.
  • The (social) scientific principle; the development of human knowledge in a systematic way – which is then shared into the democratic process, as one of a range of narratives.

Christina outlined a case study example of an initiative on period dignity which Bristol City Council is leading as part of Bristol’s One City Plan, and how the evidence base for the programme was located and used. She posed the question of what evidence matters locally, and suggested that evidence of impact, economic evidence, and retrospective evidence that demonstrates whether what has been done works, in order to build on it, are the most helpful. To close, Christina highlighted the importance of being ‘paradigm literate’ in order to navigate the complexity of public health decision making.

Academic perspective

Our final speaker, Dr Olivia Maynard, gave an academic perspective on how to advise decision makers.

Focusing on practical tips, she outlined her own work on tobacco, smoking, e-cigarettes, alcohol and other drugs and how she has engaged with various opportunities to work with policymakers.

Starting with a clear case for doing the work (it’s important, it’s interesting, to create impact), she went on to outline methods of engagement:

  • Proactively presenting your work; introduce yourself to policymakers interested in your area such as MPs, Peers, APPGs, subject specialists in parliamentary research services, advocacy groups, and PolicyBristol; review Hansard and Early Day Motions; get involved in parliamentary events
  • Respond to calls for evidence (University of Bristol researchers can find curated opportunities via the PolicyBristol PolicyScan)
  • Work directly with policymakers, for example via Policy Fellowships (for example with POST)

Olivia outlined some reflections around the differences between academia and policymaking.

Timelines for action is one, but she also used the changes towards plain packaging as an example to note that the policymaking process can span numerous years, presenting many opportunities for intervention.

She referred back to Christina’s point about ‘multiple competing realities’ to highlight that evidence is one of many factors to consider in policymaking.

She also encouraged academics to challenge ‘imposter syndrome’, by emphasising ‘you are more of an expert than you think you are’, and needing to make yourself known to be offered opportunities.

Where next?

Chair David Buck highlighted a number of themes running throughout the presentations including recognising the paradigms used by different stakeholders; questioning what counts as evidence, and being able to provide advice from an uncertain evidence base; and what these themes mean for all of us (and how willing are we to act on these reflections?)

The seminar concluded with a facilitated Q&A session spanning topics such as:

  • Should all research which influences policy be coproduced with user groups and policymakers?
  • What kind of ‘payback’ do stakeholder organisations need for their involvement in research projects?
  • How should researchers develop the skills needed to cross boundaries?
  • What funding is available for policy relevant research?
  • How can we make our evidence ‘stand out’?
  • Should academics have a responsibility to critique policy?

The seminar started numerous conversations which we hope to continue.

Chair David Buck facilitates our Q&A. Image credit: Julio Hermosilla Elgueta

Access the slides:

1. Richard Gleave Crossing boundaries – undertaking knowledge informed public health

2. Isabel Oliver Partnerships between science and society

3. Christina Gray Evidence into practice

4. Olivia Maynard An academic’s perspective

View a recording of the event on the IEU’s YouTube channel: https://www.youtube.com/watch?v=-ew-RvzV-D0 

Contact lindsey.pike@bristol.ac.uk if you’d like to hear about future events.