Tag Archives: Health

So Where Are We up to with Alcohol and Health?

First, let me come clean – I am a moderate drinker. No doubt about it. Five nights a week on a mean of two glasses, and two nights on a mean of three glasses. These are average sized glasses, so let’s say 24 units (1.5 x 16) per week. I love wine and seek good news…

The story so far:

  1. There is a ‘J-shaped’ curve of the association between alcohol and many diseases.[1]
    093 - Alcohol j curve
    * Cancer does not follow this pattern. Cancers of mouth, throat and gullet are almost certainly increased, and probably breast too.[2]
  2. But Mendelian randomisation (inheriting genes predisposing to alcohol consumption) does not show a J-shaped curve – risk rises incrementally.[3]
  3. Longitudinal studies show that, on one dimension of cognition, decline is faster in linear relationship to alcohol dose, and this finding ‘triangulates’ with a drop in right-sided hippocampal volume (detected by MRI) in relation to alcohol intake.[4]

Conclusion: the J-shaped curve is an artefact of selection bias.

So what’s new? First, a meta-analysis of longitudinal studies [5] shows a substantial protective effect against dementia for low to moderate alcohol intake (RR 0.63, 0.53-0.75) and also in Alzheimer’s disease (RR 0.57, 0.44-0.74). Second, there some evidence from these studies that chronic drinking is protective of cognitive decline, while episodic drinking is harmful at the same total intake. Third, a new longitudinal study suggests that chronic (i.e. non-binge) drinking is indeed protective against cognitive impairment in older people.[6]

This new study (the Rancho Bernardo study) is based on a cohort of 6,339 middle-class residents of a suburb in San Diego. Of the surviving residents, 2,479 attended a research clinic in 1985 where detailed alcohol histories were elicited. The participants were followed up every four years with cognitive tests. Co-variates were collected and added sequentially to a logistic regression model, starting with those (e.g. sex and age) least likely to be on the causal pathway linking alcohol to outcome. The APOE genotype was examined as an interaction term. Potential confounding effects of diet were also examined. Various sensitivity analyses were conducted. Drinking up to 3 units per day after age 65, and 4 units per day at a younger age significantly increased the chance of healthy survival, with an odds ratio exceeding 2. The J curve is there in the data, with the probability of healthy longevity increasing through no, low, moderate and even heavy drinking, only to decline again when drinking was ‘excessive’ (meaning over 4 drinks per day aged under 65 and over 3 per day for men over 65, and 3 or 2 drinks per day in younger or older women. And, yes, more frequent drinking is better than episodic drinking at a given intake – ORs of Cognitively Health Longevity increased three-fold with daily drinking vs. not drinking at all, but only two-fold if drinking was ‘infrequent’. Conclusions were robust to various sensitivity analyses.

What is the truth? No person knoweth it! But the idea that regular, moderate drinking offers some protective effects to trade-off against cancer risk has empirical support. I wonder if there are different genes predisposing to binge vs. steady drinking? I hypothesise that the genes are associated with poor impulse control leading to binge drinking. I hope that this hypothesis will now be put to an empirical test. Another question, of course, concerns the type of drink. The middle-class people in the Rancho Bernardo study may have favoured wine over other drinks – I hope so!

— Richard Lilford, CLAHRC WM Director


  1. Di Castelnuovo A, Costanzo  S, Bagnardi  V, Donati  MB, Iacoviello  L, de Gaetano    Alcohol dosing and total mortality in men and women: an updated meta-analysis of 34 prospective studies.  Arch Intern Med. 2006; 166(22): 2437-45.
  2. Lilford RJ. Oh Dear – Evidence Against Alcohol Accumulates. NIHR CLAHRC West Midlands News Blog. 7 December, 2017.
  3. Holmes MV, Dale CE, Zuccolo L, et al. Association between alcohol and cardiovascular disease: Mendelian randomisation analysis based on individual participant data. BMJ. 2014; 349: g4164.
  4. Lilford RJ. Alcohol and its Effects. NIHR CLAHRC West Midlands News Blog. 18 August, 2017.
  5. Peters R, Peters J, Warner J, Beckett N, Bulpitt C. Alcohol, dementia and cognitive decline in the elderly: a systematic review. Age Ageing. 2008; 37(5): 505-12.
  6. Richard EL, Kritz-Silverstein D, Laughlin GA, Fung TT, Barrett-Connor E, McEvoy LK. Alcohol Intake and Cognitively Healthy Longevity in Community-Dwelling Adults: The Rancho Bernardo Study. J Alzheimer’s Dis. 2017; 59: 803-14.

New Framework to Guide the Evaluation of Technology-Supported Services

Heath and care providers are looking to digital technologies to enhance care provision and fill gaps where resource is limited. There is a very large body of research on their use, brought together in reviews, which among many others, include, establishing effectiveness in behaviour change for smoking cessation and encouraging adherence to ART,[1] demonstrating improved utilisation of maternal and child health services in low- and middle-income countries,[2] and delineating the potential for improvement in access to health care for marginalised groups.[3] Frameworks to guide health and care providers when considering the use of digital technologies are also numerous. Mehl and Labrique’s framework aims to help a low- or middle-income country consider how they can use digital mobile health innovation to help succeed in the ambition to achieving universal health coverage.[4] The framework tells us what is somewhat obvious, but by bringing it together it provides a powerful tool for thinking, planning, and countering pressure from interest groups with other ambitions. The ARCHIE framework developed by Greenhalgh, et al.[5] is a similar tool but for people with the ambition of using telehealth and telecare to improve the daily lives of individuals living with health problems. It sets out principles for people developing, implementing, and supporting telehealth and telecare systems so they are more likely to work. It is a framework that, again, can be used to counter pressure from interest groups more interested in the product than the impact of the product on people and the health and care service. Greenhalgh and team have now produced a further framework that is very timely as it provides us with a tool for thinking through the potential for scale-up and sustainability of health and care technologies.[6]

Greenhalgh, et al. reviewed 28 previously published technology implementation frameworks in order to develop their framework, and use their own studies of digital assistive technologies to test the framework. Like the other frameworks this provides health and care providers with a powerful tool for thinking, planning and resisting. The Domains in the Framework include, among others, the health condition, the technology, the adopter system (staff, patients, carers), the organisation, and the Domain of time – how the technology embeds and is adapted over time. For each Domain in the Framework the question is asked whether it is simple, complicated or complex in relation to scale-up and sustainability of the technology. For example, the nature of the condition: is it well understood and predictable (simple), or poorly understood and unpredictable (complex)? Asking this question for each Domain allows us to avoid the pitfall of thinking something is simple when it is in reality complex. For example, there may be a lot of variability in the health condition between patients, but the technology may have been designed with a simplified textbook notion of the condition in mind. I suggest that even where clinicians are involved in the design of interventions, it is easy for them to forget how often they see patients that are not like the textbook, as they, almost without thinking, deploy their skills to adapt treatment and management to the particular patient. Greenhalgh, et al. cautiously conclude that “it is complexity in multiple domains that poses the greatest challenge to scale-up, spread and sustainability”. They provide examples where unrecognised complexity stops in its tracks the use of a technology.

— Frances Griffiths, Professor of Medicine in Society


  1. Free C, Phillips G, Galli L. The effectiveness of mobile-health technology-based health behaviour change or disease management interventions for health care consumers: a systematic review. PLoS Med. 2013;10:e1001362.
  2. Sondaal SFV, Browne JL, Amoakoh-Coleman M, Borgstein A, Miltenburg AS, Verwijs M, et al. Assessing the Effect of mHealth Interventions in Improving Maternal and Neonatal Care in Low- and Middle-Income Countries: A Systematic Review. PLoS One. 2016;11(5):e0154664.
  3. Huxley CJ, Atherton H, Watkins JA, Griffiths F. Digital communication between clinician and patient and the impact on marginalised groups: a realist review in general practice. Br J Gen Pract. 2015;65(641):e813-21.
  4. Mehl G, Labrique A. Prioritising integrated mHealth strategies for universal health coverage. Science. 2014;345:1284.
  5. Greenhalgh T, Procter R, Wherton J, Sugarhood P, Hinder S, Rouncefield M. What is quality in assisted living technology? The ARCHIE framework for effective telehealth and telecare services. BMC Medicine. 2015;13(1):91.
  6. Greenhalgh T, Wherton J, Papoutsi C, Lynch J, Hughes G, A’Court C, et al. Beyond Adoption: A New Framework for Theorizing and Evaluating Nonadoption, Abandonment, and Challenges to the Scale-Up, Spread, and Sustainability of Health and Care Technologies. J Med Internet Res. 2017;19(11):e367.

An Issue of BMJ with Multiple Studies on Diet

This News Blog often contains information about diet and health. For example, we have cited evidence that salt is enemy number one [1]; trans-fats are unremittingly bad news [2]; and large amounts of sugar are harmful.[3] After that the risks become really rather small – relative risks of about 20%. Fruit, and more especially vegetables, are good news. Milk is an unhealthy drink in adults (never intended for that purpose and galactose is harmful, unless removed during a fermentation process).[4] Three further studies of diet were included in a single recent issue of the BMJ.[5-7]

The first study by Etemadi, et al. looked at meat consumption in a large cohort of people (n= 536,969) who gave detailed dietary histories.[5] The evidence corroborates other studies in showing that red meat is harmful, increasing relative risk of death by about 20% in high meat eaters compared to moderate meat eaters. The difference is greater if the comparison is made with people who obtain almost all of their meat in the form of fish and chicken. The causes of death that showed greatest increases in risk with high red meat consumption were cancer, respiratory disease and liver disease. Surprisingly, perhaps, increased risk from stroke was nugatory. The increased risk in unprocessed meat is probably related to haem iron, and in processed meat to nitrates/nitrites – there are all pro-oxidant chemicals. Of course this is an association study, so some uncertainty remains. The main problem with meat, as the BMJ Editor points out,[8] is the harmful environmental effects; apparently animal husbandry contributes more to global warming than burning fossil fuels. I take the environmental effects seriously – perhaps we will one day vilify meat farmers more vociferously than we currently vilify tobacco farmers. After all, individuals don’t have to smoke, but cannot protect themselves from the harmful effects of pollution.

Meanwhile, for those who are interested, the other two relevant articles in this issue of the BMJ looked at avoiding gluten in people who do not have celiac disease (no benefit and evidence points towards harm),[6] and the beneficial effect of a low salt and fat diet on gout.[7]

— Richard Lilford, CLAHRC WM Director


  1. Lilford RJ. Effects of Salt in Diet. NIHR CLAHRC West Midlands News Blog. 17 October 2014.
  2. Lilford RJ. On Diet Again. NIHR CLAHRC West Midlands News Blog. 23 October 2015.
  3. Lilford RJ. How Much Sugar is Too Much? NIHR CLAHRC West Midlands News Blog. 25 September 2015.
  4. Lilford RJ. Two Provocative Papers on Diet and Health. NIHR CLAHRC West Midlands News Blog. 12 December 2014.
  5. Etemadi A, Sinha R, Ward MH, Graubard BI, Inoue-Choi M, Dawsey SM, Abnet CC. Mortality from different causes associated with meat, heme iron, nitrates, and nitrites in the NIH-AARP Diet and Health Study: population based cohort study. BMJ. 2017; 357: j1957.
  6. Lebwohl B, Cao Y, Zong G, Hu FB, Green PHR, Neugut AI, Rimm EB, Sampson L, Dougherty LW, Giovannucci E, Willett WC, Sun Q, Chan AT. Long term gluten consumption in adults without celiac disease and risk of coronary heart disease: prospective cohort study. BMJ. 2017; 357: j1892.
  7. Rai SK, Fung TT. Lu N, Keller SF, Curhan GC, Choi HK. The Dietary Approaches to Stop Hypertension (DASH) diet, Western diet and risk of gout in men: prospective cohort study. BMJ. 2017; 357: j1794.
  8. Godlee F. Red meat: another inconvenient truth. BMJ. 2017; 357: j2278.

‘Information is not knowledge’: Communication of Scientific Evidence and how it can help us make the right decisions

Every one of us is required to make many decisions: from small decisions, such as what shoes to wear with an outfit or whether to have a second slice of cake; to larger decisions, such as whether to apply for a new job or what school to send our children to. For decisions where the outcome can have a large impact we don’t want to play a game of ‘blind man’s buff’ and make a decision at random. We do our utmost to ensure that whatever decision we arrive at, it is the right one. We go through a process of getting hold of information from a variety of sources we trust and processing that knowledge to help us make up our minds. And in this digital age, we have access to more information than ever before.

When it comes to our health, we are often invited to be involved in making shared decisions about our own care as patients. Because it’s our health that’s at stake, this can bring pressures of not only making a decision but also making the right decision. Arriving at a wrong decision can have significant consequences, such as over- or under-medication or missing out from advances in medicine. But how do we know how to make those decisions and where do we get our information from? Before we start taking a new course of medication, for example, how can we find out if the drugs are safe and effective, and how can we find out the risks as well as the benefits?

The Academy of Medical Sciences produced a report, ‘Enhancing the use of scientific evidence to judge the potential benefits and harms of medicine’,[1] which examines what changes would be necessary to help patients make better-informed decisions about taking medication. It is often the case that there is robust scientific evidence that can be useful in helping patients and clinicians make the right choices. However, this information can be difficult to find, hard to understand, and cast adrift in a sea of poor-quality or misleading information. With so much information available, some of it conflicting – is it any surprise that in a Medical Information Survey, almost two-thirds of British adults would trust experiences of friends and family compared to data from clinical trials, which only 37% of British adults would trust?[2]

The report offers recommendations on how scientific evidence can be made available to enable people to weigh up the pros and cons of new medications and arrive at a decision they are comfortable with. These recommendations include: using NHS Choices as a ‘go to’ hub of clear, up-to-date information about medications, with information about benefits and risks that is easy to understand; improving the design, layout and content of patient information leaflets; giving patients longer appointment times so they can have more detailed discussions about medications with their GP; and a traffic-light system to be used by the media to endorse the reliability of scientific evidence.

This is all good news for anyone having to decide whether to start taking a new drug. I would welcome the facility of going to a well-designed website with clear information about the risks and benefits of taking particular drugs rather than my current approach of asking friends and family (most of whom aren’t medically trained), searching online, and reading drug information leaflets that primarily present long lists of side-effects.

Surely this call for clear, accessible information about scientific evidence is just as relevant to all areas of medical research, including applied health. Patients and the public have a right to know how scientific evidence underpinning important decisions in care is generated and to be able to understand that information. Not only do patients and the public also make decisions about aspects of their care, such as whether to give birth at home or in hospital, or whether to take a day off work to attend a health check, but they should also be able to find and understand evidence that explains why care is delivered in a particular way, such as why many GPs now use a telephone triage system before booking in-person appointments. Researchers, clinicians, patients and communicators of research all have a part to play.

In CLAHRC West Midlands, we’re trying to ‘do our bit’. We aim to make accessible a sound body of scientific knowledge through different information channels and our efforts include:

  • Involving patients and the public to write lay summaries of our research projects on our website so people can find out about the research we do.
  • Communication of research evidence in accessible formats, such as CLAHRC BITEs, which are reviewed by our Public Advisors.
  • Method Matters, a series aimed to give members of the public a better understanding of concepts in Applied Health Research.

The recommendations from the Academy of Medical Sciences can provide a useful starting point for further discussions on how we can communicate effectively in applied health research and ensure that scientific evidence, rather than media hype or incomplete or incorrect information, is the basis for decision-making.

— Magdalena Skrybant, CLAHRC WM PPIE Lead


  1. The Academy of Medical Sciences. Enhancing the use of scientific evidence to judge the potential benefits and harms of medicine. London: Academy of Medical Sciences; 2017.
  2. The Academy of Medical Sciences. Academy of Medical Sciences: Medical Information Survey. London: Academy of Medical Sciences; 2016

Private Consultations More Effective than Public Provision in Rural India

Doing work across high-income countries (CLAHRC WM) and lower income countries (CLAHRC model for Africa) provides interesting opportunities to compare and contrast. For example, our work on user fees in Malawi [1] mirrors that in high-income countries [2] – in both settings, relatively small increments in out-of-pocket expenses results in a large decrease in demand and does so indiscriminately (the severity of disease among those who access services is not shifted towards more serious cases). However, the effect of private versus public provision of health care is rather more nuanced.

News Blog readers are likely aware of the famous RAND study in the US.[3] People were randomised to receive their health care on a fee-for-service basis (‘privately’) vs. on a block contract basis (as in a public service). The results showed that fee-for-service provision resulted in more services being provided (interpreted as over-servicing), but that patients were more satisfied clients, compared to those experiencing public provision. Clinical quality was no different. In contrast, a study from rural India [4] found that private provision results in markedly improved quality compared to public provision, albeit with a degree of over-servicing.

The Indian study used ‘standardised patients’ (SPs) to measure the quality of care during consultations covering three clinical scenarios – angina, asthma and the parent of a child with dysentery. The care SPs received was scored against an ideal standard. Private providers spent more time/effort collecting the data essential for making a correct diagnosis, and were more likely to give treatment appropriate to the condition. First, they compared private providers with public providers and found that the former spent 30% more time gathering information from the SPs than the public providers. Moreover, the private providers were more likely to be present when the patient turned up for a consultation. There was a positive correlation between the magnitude of fees charged by private providers and time spent eliciting symptoms and signs, and the probability that the correct treatment would be provided. However, the private providers are often not doctors, so this result could reflect different professional mix, at least in part. To address this point, a second study was done whereby the same set of doctors were presented with the same clinical cases – a ‘dual sample’. The results were even starker, with doctors spending twice as long with each patient when seen privately.

Why were these results from rural India so different from the RAND study? The authors suggest that taking a careful history and examination is part of the culture for US doctors, and that they had reach a kind of asymptote, such that context made little difference to this aspect of their behaviour. Put another way, there was little headroom for an incentive system to drive up quality of care. However, in low-income settings where public provision is poorly motivated and regulated, fee-for-service provision drives up quality. The same seems to apply to education, where private provision was found to be of higher quality than public provision in low-income settings – see previous News Blog.[5]

However, it should be acknowledged that none of the available alternatives in rural India were good ones. For example, the probability of receiving the correct diagnosis varied across the private and public provider, but never exceeded 15%, while the rate of correct treatment varied from 21% to about 50%. Doctors were more likely than other providers to provide the correct diagnosis. A great deal of treatment was inappropriate. CLAHRC West Midlands’ partner organisation in global health is conducting a study of service provision in slums with a view to devising affordable models of improving health care.[6]

— Richard Lilford, CLAHRC WM Director


  1. Watson SI, Wroe EB, Dunbar EL, et al. The impact of user fees on health services utilization and infectious disease diagnoses in Neno District, Malawi: a longitudinal, quasi-experimental study. BMC Health Serv Res. 2016; 16: 595.
  2. Carrin G & Hanvoravongchai P. Provider payments and patient charges as policy tools for cost-containment: How successful are they in high-income countries? Hum Resour Health. 2003; 1: 6.
  3. Brook RH, Ware JE, Rogers WH, et al. The effect of coinsurance on the health of adults. Results from the RAND Health Insurance Experiment. Santa Monica, CA: RAND Corporation, 1984.
  4. Das J, Holla A, Mohpal A, Muralidharan K. Quality and Accountability in Healthcare Delivery: Audit-Study Evidence from Primary Care in India . Am Econ Rev. 2016; 106(12): 3765-99.
  5. Lilford RJ. League Tables – Not Always Bad. NIHR CLAHRC West Midlands News Blog. 28 August 2015.
  6. Lilford RJ. Between Policy and Practice – the Importance of Health Service Research in Low- and Middle-Income Countries. NIHR CLAHRC West Midlands News Blog. 27 January 2017.

Exercise and Energy Expenditure: Not What You Think?

Each week I burn up to 1,500 kcals in my two hours of intense ‘spinning’. So you might have thought (like me) that I could indulge in 1,500 kcals worth of extra puddings. Well you (like me) would have thought wrong, at least according to careful animal and human studies described by Pontzer in this month’s Scientific American.[1] Apparently, short of being an absolute coach potato or an extreme sportsman like Mark Spitz, the rest of us burn the same number of Calories per day, adjusted for mass, irrespective of how much we exercise. Apparently the body compensates for activity by consuming less Calories at rest. Says Pontzer, “exercise to stay healthy, but restrict Calories to control weight

Richard Lilford, CLAHRC WM Director


  1. Pontzer H. The Exercise Paradox. Scientific American. February 2017.


Inequalities: Your Next Exciting Instalment

One month ago we cited the majestical study of health and wealth published in JAMA.[1] A fortnight ago we cited Angus Deaton’s insightful commentary on this study.[2] This week we draw your attention to a study of wealth and health inequalities, based on panel data (derived from national censuses) in eleven European countries covering two decades from 1990 to 2010.[3] The study was designed to look for associations between socio-economic class recorded in the censuses and deaths, overall and in major categories, such as cardiovascular disease and cancer. They also re-categorised deaths in classes that may indicate behaviours, such as smoking and alcohol. An overall reduction in age-specific mortality was observed over the study period. The study also showed that inequalities were growing wider when relative risks were compared, but absolute differences declined in nine of the eleven countries (including England and Wales). Absolute inequalities in smoking related deaths declined, but they increased for alcohol-related deaths.

— Richard Lilford, CLAHRC WM Director


  1. Chetty R, Stepner M, Abraham S, et al. The Association Between Income and Life Expectancy in the United States, 2001-2014. JAMA. 2016; 315(6):1750-66.
  2. Deaton A. On Death and Money. History, Facts, and Explanations. JAMA. 2016; 315(16): 1703-5.
  3. Mackenbach JP, Kulhánovâ I, Artnik B, et al. Changes in Mortality Inequalities over Two Decades: Register Based Study of European Countries. BMJ. 2016; 353: i1732.

Income, Relative Income and Health

News Blog readers who enjoyed my analysis of Chetty’s monumental JAMA article on income and longevity at age 40 [1] may wish to read a commentary by last year’s Nobel prize winner for economics, Angus Deaton.[2] Some points:

  1. Studies correlating income with longevity over-estimate the association between wealth and age because they assume that people to whom the results are extrapolated will remain in their income groups.
  2. The association between wealth and health overestimates the causal effect of wealth on health because health also influences wealth to a degree.
  3. While the life expectancy of poor people varies widely by locality, those of rich people does not.
  4. Given the poor health of middle-aged Americans, especially white Americans from low socio-economic levels, we can expect to see health disparities of adults widen in the short-term. Health disparities in children in America are declining (see previous post).
  5. In setting policy – especially tax rates – be guided by absolute not relative income disparities. Every society has a top and bottom percentile and always will have; just like more than half of people cannot be above median.
  6. Be careful when someone tells you that health disparities are growing – often (as now) relative disparities widen as absolute disparities decline. This can happen because the same relative risk reduction has a bigger (absolute) effect when baseline rates of ill-health are high (as among poor people) than when they are low (as among the financially better-off).
  7. Education and cognitive ability are independent predictors of both health and wealth. Since parents are important educators, the regress is hard to break.

— Richard Lilford, CLAHRC WM Director


  1. Chetty R, Stepner M, Abraham S, et al. The Association Between Income and Life Expectancy in the United States, 2001-2014.JAMA. 2016; 315(6):1750-66.
  2. Deaton A. On Death and Money. History, Facts, and Explanations. JAMA. 2016; 315(16): 1703-5.

Effect of Financial Penalties on Health Outcomes

A CLAHRC WM study showed that financial penalties implemented in the West Midlands region, but not elsewhere, resulted in a sharp increase in a desired outcome – increased use of home haemodialysis.[1] A time series study of financial penalties for readmission of patients with certain target conditions (heart failure, pneumonia, and myocardial infarction) found a step change reduction in 30 day readmission rates from 22% to 18% after the implementation of the penalty.[2] Interestingly, readmissions for non-targeted conditions decreased by a similar amount. Use of observation-units was increasing gradually throughout the observation period, so it was possible that some readmissions were circumvented by sequestering patients in the observation-unit. However, hospitals with a greater increase in such wards did not have a greater reduction in readmission rates. Whether there was a price to pay in sick patients being sent home we cannot say. Also, in the absence of contemporaneous controls it is not clear that the change was caused by the intervention. The intervention was instituted as a result of a general concern about readmission rates among commissioners and providers, such that the change could have occurred as part of a ‘rising tide’.[3]

— Richard Lilford, CLAHRC WM Director


  1. Combes G, Allen K, Sein K, Girling A, Lilford R. Taking hospital treatments home: a mixed methods case study looking at the barriers and success factors for home dialysis treatment and the influence of a target on uptake rates. Implement Sci. 2015; 10: 148.
  2. Zuckerman RB, Sheingold SH, Orav J, Ruhter J, Epstein AM. Readmissions, Observation, and the Hospital Readmissions Reduction Program. New Engl J Med. 2016; 374: 1543-51.
  3. Chen YF, Hemming K, Stevens AJ, Lilford RJ. Secular trends and evaluation of complex interventions: the rising tide phenomenon. BMJ Qual Saf. 2015. [ePub].

Reversible Environmental Factors and the Global Burden of Disease

The Global Burden of Disease study is an extraordinary collaborative effort to document the health of the human race. It produces a series of weighty publications every four years, packed with interesting detail. The most recent set of papers have been published and the first deals with life years lost.[1] The study documents the recent epidemiological transition in which non-infectious diseases have taken over from infectious diseases as the main cause of life-years lost across the world. Childhood malnutrition is no longer enemy number one, relegated to fourth place globally, but it retains the number one slot in sub-Saharan Africa. High blood pressure, smoking and obesity now occupy the first three slots globally. CLAHRC Africa includes a programme of research on salt. Salt is now enemy number two, after smoking, in unhealthy behaviours. Research into methods to reduce salt intake is a priority, even as the debate continues into whether sodium levels can fall too low – some data suggest a J-shaped distribution of risk with rising salt intake. Unsafe sex is the major risk factor in East, and Southern Africa, while South Africa is the country with the world’s highest burden of disease associated with reversible environmental factors. Areca nut (another interest of CLAHRC Africa) does not make it onto the list. Along with smokeless tobacco, the CLAHRC WM Director thinks this risk should be considered for inclusion in further versions. Another criticism is double counting – high sodium intake and high systolic blood pressure both appear on the list, yet the former is a prominent cause of the latter. To be fair, the authors do recognise this issue. In a future blog we will report on a further analysis of the remarkable GBD dataset to consider not just the deaths, but the total burden of disease (for instance in Disability Adjusted Life Years [DALYs]).

— Richard Lilford, CLAHRC WM Director


  1. GBD 2013 Risk Factor Collaborators. Global, regional, and national comparative risk assessment of 79 behavioural, environmental and occupational, and metabolic risks or clusters of risks in 188 countries, 1990–2013: a systematic analysis for the Global Burden of Disease Study 2013. Lancet. 2015; 386: 2287-323.