Tag Archives: Mortality

Association Between Cigarette Price and Infant Mortality

In an effort to reduce smoking rates governments often increase the taxation levied on cigarettes. Previous research has shown that this is an effective strategy, including improvements in child health outcomes. However, tobacco companies often use differential pricing strategies to move the increased taxation on to their premium cigarettes. This lessens the effectiveness of increased taxes as it allows people to switch to the cheaper cigarettes instead. Researchers from Imperial College London set out to assess any associations between price rises, differential pricing (using data on the minimum and median cigarette prices) and infant mortality across 23 European countries.[1] This longitudinal study looked at more than 53.7m live births over a period of ten years. During this time the authors found that a median increase of €1 per pack of cigarettes was associated with 0.23 fewer deaths per 1000 live births in the year of the price hike (95% CI, -0.37 to -0.09), and a decline of 0.16 deaths per 1000 live births in the subsequent year (95% CI, -0.30 to -0.03). Using a counterfactual scenario, the authors estimated that, overall, cigarette price increases were associated with 9,208 fewer infant deaths (i.e. if cigarette prices had remained unchanged then there would have been 9,208 more deaths). Analysis of the price differentials showed that a 10% increase in the differential between the minimum and median priced cigarettes was associated with 0.07 more deaths per 1,000 live births the following year. Further, had there been no cost differential, they estimated that 3,195 infant deaths could have been avoided.

So, while increasing cigarette taxation can have a positive effect, there needs to be more of an effort to try to eliminate budget cigarettes. This is especially true in low-income countries where price differentials tend to be significantly higher than in high-income countries.

— Peter Chilton, Research Fellow


  1. Filippidis FT, Laverty AA, Hone T, Been JV, Millett C. Association of Cigarette Price Differentials With Infant Mortality in 23 European Union Countries. JAMA Pediatr. 2017.

How Much Fruit and Veg is Enough?

We are often told that we should be eating five (or is it now ten?) portions of fruit and vegetables each day to protect against, amongst other things, cardiovascular disease (CVD).[1] However, such recommendations are generally based on research conducted in people from Europe, the USA, Japan and China. There is little data from countries in the Middle East, South America, Africa or South Asia.

The PURE study (Prospective Urban Rural Epidemiology) set out to rectify this, recruiting 135,000 participants from 18 countries, ranging from high-income countries, such as Sweden, to low-income countries, such as India.[2] The research team documented the diet of these individuals at baseline (using questionnaires specific to each country), then followed them up for a median of 7.4 years, looking at cardiovascular-related clinical outcomes. As expected higher intakes of fruit, vegetables and legumes were associated with lower incidences of major CVD, myocardial infarction, and mortality (cardiovascular-related and all-cause). However, the hazard ratio for all-cause mortality was lowest for three to four servings (375-400g) per day (0.78, 95%CI 0.69-0.88), with no significant decrease with higher consumption.

It is more likely that consuming around 375g of fruit/vegetables/legumes per day will be within the financial reach of people living in poorer countries, compared to the various recommendations of 400-800g that are often seen in Europe and North America. Before we ditch that extra snack of carrot sticks, however, it is important to note that factors such as food type, nutritional quality, cultivation and preparation are likely to vary between countries, while other clinical outcomes, such as cancer, were not looked at in this study.

The authors are continuing to enrol more participants, and are hoping to re-examine their results in the future.

— Peter Chilton, Research Fellow


  1. Oyebode O, Gordon-Dseagu V, Walker A, Mindell JS. Fruit and vegetable consumption and all-cause, cancer and CVD mortality: analysis of Health Survey for England data. J Epidemiol Community Health. 2014; 68(9): 856-62.
  2. Miller V, Mente A, Dehghen M, et al. Fruit, vegetable, and legume intake, and cardiovascular disease and deaths in 18 countries (PURE): a prospective cohort study. Lancet. 2017.

Should You Keep Drinking Coffee?

It is nice, at last, to find something that is it is really enjoyable and that is good for us. Recent News Blogs have carried articles on the harmful effects of red meat,[1] milk,[2] and alcohol.[3] But what about coffee? A recent article, based on over 500,000 people in ten European countries confirmed the already extensive literature showing that coffee is beneficial for health.[4] In fact, overall death rates were reduced by over 10%. There was a massive (over 50%) reduction in diseases of the digestive system, confirming the well-known beneficial effect of coffee on the liver. The trend was also favourable for heart disease and stroke. Many biochemical markers also moved in a favourable direction, including glycated haemoglobin, and C-reactive protein. The only bit of bad news pertained to ovarian cancer, where a 30% increased risk of death was noted. Reverse causality is always a possibility in non-experimental studies, even if, like this one, they are prospective. However, this is unlikely since the hazard ratios were unaltered if patients who died within eight years of recruitment were excluded.

— Richard Lilford, CLAHRC WM Director


  1. Lilford RJ. An Issue of BMJ with Multiple Studies on Diet. NIHR CLAHRC West Midlands News Blog. 4 August 2017.
  2. Lilford RJ. Two Provocative Papers on Diet and Health. NIHR CLAHRC West Midlands News Blog. 12 December 2014.
  3. Lilford RJ. Alcohol and its Effects. NIHR CLAHRC West Midlands News Blog. 18 August 2017.
  4. Gunter MJ, Murphy N, Cross AJ, et al. Coffee Drinking and Mortality in 10 European Countries. Ann Intern Med. 2017; 167: 236-47.

“We seek him here, we seek him there, Those Frenchies seek him everywhere.”

The notorious weekend mortality effect is every bit as elusive as the Scarlet Pimpernel. Recent studies have delved deeper into the possibility that the weekend effect is an artefact of admission of sicker patients at the weekend than on week days.[1] First, it has been shown that the mortality of all who present to the emergency department (i.e. admitted plus sent home) is the same over the weekend as over the rest of the week.[2] Second, patients who arrive by ambulance are generally much sicker than patients arriving by other means and the proportion who arrive by ambulance is higher over the weekend than over weekdays.[3] When controlling for method of arrival, most of the weekend effect disappears. Most, but not all. This paper provides further evidence that most estimates of the weekend effect are at least overestimates. Through Professor Julian Bion’s HiSLAC Study [4] we are evaluating the effect of weekend admission, not just on mortality, but also on the quality of care and the overall adverse event rate. We will use a Bayesian network to synthesise information across the causal chain and come up with a refined estimate of the effect of weekend admission, not only on mortality, but also on other adverse events.

— Richard Lilford, CLAHRC WM Director


  1. Bray BD, Steventon A. What have we learnt after 15 years of research into the ‘weekend effect’? BMJ Qual Saf. 2017; 26: 607-10.
  2. Aldridge C, Bion J, Boyal A, et al. Weekend specialist intensity and admission mortality in acute hospital trusts in England: a cross-sectional study. Lancet. 2016. 388: 178-86.
  3. Anselmi L, Meacock R, Kristensen SR, Doran T, Sutton M. Arrival by ambulance explains variation in mortality by time of admission: retrospective study of admissions to hospital following emergency department attendance in England. BMJ Qual Saf. 2017; 26: 613-21.
  4. Chen Y, Boyal A, Sutton E, et al. The magnitude and mechanisms of the weekend effect in hospital admissions: A protocol for a mixed methods review incorporating a systematic review and framework synthesis. Syst Rev. 2016; 5: 84.

Declining Readmission Rates – Are They Associated with Increased Mortality?

I have always been a bit nihilistic about reducing readmission rates to hospitals.[1][2] However, I may have been overly pessimistic. A new study confirms that it is possible to reduce readmission rates by imposing financial incentives.[3] Importantly, this does not seem to have caused an increase in mortality – as might occur if hospitals were biased against re-admitting sick patients in order to avoid a financial penalty. “False null result” (type two error), do I hear you ask? Probably not, since the data are based on nearly seven million admissions. In fact, 30 day mortality rates were slightly lower among hospitals that reduced readmission rates.

— Richard Lilford, CLAHRC WM Director


  1. Lilford RJ. If Not Preventable Deaths, Then What About Preventable Admissions? NIHR CLAHRC West Midlands News Blog. 6 May 2016.
  2. Lilford RJ. Unintended Consequences of Pay-For-Performance Based on Readmissions. NIHR CLAHRC West Midlands News Blog. 13 January 2017.
  3. Joynt KE, & Maddox TM. Readmissions Have Declined, and Mortality Has Not Increased. The Importance of Evaluating Unintended Consequences. JAMA. 2017; 318(3): 243-4.

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.

Another Excellent Paper on Economic and Mortality Inequality from Currie & Schwandt

In the latest News Blog [1] (before the election purdah) I covered Case and Deaton’s monumental study of death rates among white people in the US.[2] I briefly mentioned the idea that childhood (and even pre-natal) exposure can ‘programme’ the body, leading to mortality differences in later life. This can lead to exaggerated estimates of the effects of economic conditions and behaviours in later life on health and life expectancy. There is strong evidence that patterns of behaviour in adulthood are laid down by the age of three.[3] Failure to give due consideration to prior conditions can also lead to poor interpretation of life expectancy statistics. Life expectancy (say at birth) is derived, perforce, from the current age-specific mortality rates at all (subsequent) ages.[4] So there is an assumption that when a baby born in 2017 reaches age 40, she or he will be subject to the current mortality rates for 40 year olds, and so on. That is a massive assumption, given the above point concerning early childhood effects on adult health.

The subject of wealth and health is replete with academic bear traps. Mortality is rising among poor white people,[5] as we pointed out in a previous News Blog.[6] But then the composition of poor white people changes over time. So the mortality of poor white 40 year old women cannot automatically be ascribed to any recent change in the behaviours or exposures of such women. It could be attributable to their early life exposures. Likewise, Hispanic children have been dropping out of school in the US at progressively lower rates. So any observation comparing the health of drop-outs over time is highly biased – the same types of people are not being compared. And when it comes to ethnicity, things get harder still because the way ethnic groups are classified is ephemeral.

Currie and Schwandt use counties in the US as the basis for comparative statistics.[7] They use three year averages to reduce noise, and they measure the socio-economic standards of counties in different ways – poverty rates, high-school completion rates, and median income. They look at age-specific death rates, life expectancy (as a consolidated measure of death rates over all ages), and age/sex adjusted differences by race.

What do they find in their study covering the years 1990-2010?

  1. Life expectancy is increasing across the US, but us doing so to a greater extent in poorer areas than in richer ones.
  2. This relative improvement in poor counties compared to rich counties is seen particularly among women.
  3. And in children under the age of five (see a previous News Blog [6]).
  4. Inequalities in death rates in young adults are also declining.
  5. But over age 50, inequalities in mortality increased for women while remaining unchanged in men.
  6. For black children, inequalities narrowed compared to white children.
  7. The increased health inequality of white adults cited in our last News Blog is confirmed (phew!).

There are other interesting findings. I would have thought that immigrants would have worse outcomes than age and race matched residents, but the opposite is the case – at least for Hispanic people. A massive study of identical twins separated at birth would be needed to sort out cause and effect relationships (and even that would not be perfect). However, taken in the round, the news from the USA is good regarding inequalities; poor white people aside. Let me therefore end with a quote from the article – you can make of it what you will:

It sometimes seems as if the research literature on mortality is compelled in some way to emphasize a negative message, either about a group that is doing less well or about some aspect of inequality that is rising.

— Richard Lilford, CLAHRC WM Director


  1. Lilford RJ. Ever Increasing Life Expectancies Come to an Abrupt End Among American Whites. NIHR CLAHRC West Midlands News Blog. 5 May 2017.
  2. Case A, & Deaton A. Mortality and morbidity in the 21st century. Brookings Papers on Economic Activity. BPEA Conference Drafts. March 23-24, 2017.
  3. Suzuki E, & Fantom N. What does “life expectancy at birth” really mean? The DATA Blog. 11 November 2013.
  4. Lilford RJ. More on Brain Health in Young Children and Effect on Life Course. NIHR CLAHRC West Midlands News Blog. 24 February 2017.
  5. 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.
  6. Lilford RJ. Relative Wealth and Health. NIHR CLAHRC West Midlands News Blog. 6 May 2016.
  7. Currie J & Schwandt H. Mortality Inequality: The Good News from a County-Level Approach. J Econ Perspect. 2016; 30(2): 29-52.

Measuring Quality of Care

Measuring quality of care is not a straightforward business:

  1. Routinely collected outcome data tend to be misleading because of very poor ratios of signal to noise.[1]
  2. Clinical process (criterion based) measures require case note review and miss important errors of omission, such as diagnostic errors.
  3. Adverse events also require case note review and are prone to measurement error.[2]

Adverse event review is widely practiced, usually involving a two-stage process:

  1. A screening process (sometimes to look for warning features [triggers]).
  2. A definitive phase to drill down in more detail and refute or confirm (and classify) the event.

A recent HS&DR report [3] is important for two particular reasons:

  1. It shows that a one-stage process is as sensitive as the two-stage process. So triggers are not needed; just as many adverse events can be identified if notes are sampled at random.
  2. In contrast to (other) triggers, deaths really are associated with a high rate of adverse events (apart, of course, from the death itself). In fact not only are adverse events more common among patients who have died than among patients sampled at random (nearly 30% vs. 10%), but the preventability rates (probability that a detected adverse event was preventable) also appeared slightly higher (about 60% vs. 50%).

This paper has clear implications for policy and practice, because if we want a population ‘enriched’ for high adverse event rates (on the ‘canary in the mineshaft’ principle), then deaths provide that enrichment. The widely used trigger tool, however, serves no useful purpose – it does not identify a higher than average risk population, and it is more resource intensive. It should be consigned to history.

Lastly, England and Wales have mandated a process of death review, and the adverse event rate among such cases is clearly of interest. A word of caution is in order here. The reliability (inter-observer agreement) in this study was quite high (Kappa 0.5), but not high enough for comparisons across institutions to be valid. If cross-institutional comparisons are required, then:

  1. A set of reviewers must review case notes across hospitals.
  2. At least three reviewers should examine each case note.
  3. Adjustment must be made for reviewer effects, as well as prognostic factors.

The statistical basis for these requirements are laid out in detail elsewhere.[4] It is clear that reviewers should not review notes from their own hospitals, if any kind of comparison across institutions is required – the results will reflect the reviewers rather than the hospitals.

Richard Lilford, CLAHRC WM Director


  1. Girling AJ, Hofer TP, Wu J, et al. Case-mix adjusted hospital mortality is a poor proxy for preventable mortality: a modelling studyBMJ Qual Saf. 2012; 21(12): 1052-6.
  2. Lilford R, Mohammed M, Braunholtz D, Hofer T. The measurement of active errors: methodological issues. Qual Saf Health Care. 2003; 12(s2): ii8-12.
  3. Mayor S, Baines E, Vincent C, et al. Measuring harm and informing quality improvement in the Welsh NHS: the longitudinal Welsh national adverse events study. Health Serv Deliv Res. 2017; 5(9).
  4. Manaseki-Holland S, Lilford RJ, Bishop JR, Girling AJ, Chen YF, Chilton PJ, Hofer TP; UK Case Note Review Group. Reviewing deaths in British and US hospitals: a study of two scales for assessing preventability. BMJ Qual Saf. 2016. [ePub].

An Interesting Report of Quality of Care Enhancement Strategies Across England, Germany, Sweden, the Netherlands, and the USA

An interesting paper from the Berlin University of Technology compares the quality enhancement systems across the above countries with respect to measuring, reporting and rewarding quality.[1] This paper is an excellent resource for policy and health service researchers. The US has the most developed system of quality-related payments (P4P) of the five countries. England wisely uses only process measures to reward performance, while the US and Germany include patient outcomes. The latter are unfair because of signal to noise issues,[2] and the risk-adjustment fallacy.[3] [4] Above all, remember Lilford’s axiom – never base rewards or sanctions on a measurement over which service providers do not feel they have control.[5] It is true, as the paper argues, that rates of adherence to a single process seldom correlate with outcome. But this is a signal to noise problem. ‘Proving’ that processes are valid takes huge RCTs, even when the process is applied to 0% (control arm) vs. approaching 100% (intervention arm) of patients. So how could an improvement from say 40% to 60% in adherence to clinical process show up in routinely collected data?[6] I have to keep on saying it – collect outcome data, but in rewarding or penalising institutions on the basis of comparative performance – process, process, process.

— Richard Lilford, CLAHRC WM Director


  1. Pross C, Geissler A, Busse R. Measuring, Reporting, and Rewarding Quality of Care in 5 Nations: 5 Policy Levers to Enhance Hospital Quality Accountability. Milbank Quart. 2017; 95(1): 136-83.
  2. Girling AJ, Hofer TP, Wu J, et al. Case-mix adjusted hospital mortality is a poor proxy for preventable mortality: a modelling study. BMJ Qual Saf. 2012; 21: 1052-6.
  3. Mohammed MA, Deeks JJ, Girling A, et al. Evidence of methodological bias in hospital standardised mortality ratios: retrospective database study of English hospitals. BMJ. 2009; 338: b780.
  4. Lilford R, & Pronovost P. Using hospital mortality rates to judge hospital performance: a bad idea that just won’t go away. BMJ. 2010; 340: c2016.
  5. Lilford RJ. Important evidence on pay for performance. NIHR CLAHRC West Midlands News Blog. 20 November 2015.
  6. Lilford RJ, Chilton PJ, Hemming K, Girling AJ, Taylor CA, Barach P. Evaluating policy and service interventions: framework to guide selection and interpretation of study end points. BMJ. 2010; 341: c4413.

Can Thinking Make It So?

When we think of risk factors for mortality we properly think behaviours (e.g. smoking / obesity) or genetics (e.g. family history). What about psychological factors – can unhappiness increase your risk of risk of cancer? Well, Batty and colleagues [1] have tackled this problem as follows:

  1. They assembled 16 prospective cohort studies where behaviours and psychological state had been measured and in which participants were followed up to see if cancer developed.
  2. They obtained the raw data and obtained an individual patient meta-analysis.
  3. They adjusted for the usual things known to increase risk of cancer (obesity, smoking, etc).
  4. They calculated relative risk of cancer according to antecedent psychological state.

They found a positive correlation between psychological distress and risk of cancer. But causality might have run the other way – (occult) cancers may have been the cause of psychological distress, not the other way round. So:

  1. They ‘left censored’ the data, thereby widening the gap between the point in time where the psychological state was measured and the point where cancer supervened.

The association between psychological state and cancer death persisted, even when they were separated by many years. What is the explanation?

  1. Failure to fully control for all behaviours (although behaviour could be the mechanism through which the cancer risk is increased in people with depression, in which case they ‘over-controlled’).
  2. Reduced natural killer cell function.
  3. Increased steroid levels, which can apparently affect DNA repair in some way.
  4. Some mechanism yet to be discovered.

In any event, the findings are intriguing, for all that practical implications may be limited.

— Richard Lilford, CLAHRC WM Director


  1. Batty GD, Russ TC, Stamatakis E, Kivimäki M. Psychological distress in relation to site specific cancer mortality: pooling of unpublished data from 16 prospective cohort studies. BMJ. 2017; 356: j108.