Habit (re)forming
Behavioural risk factors such as smoking and excessive alcohol consumption are significant drivers of mortality and morbidity. In 2014, the UK NHS estimated smoking to be responsible for 78,000 deaths, or 17% of all deaths recorded that year. By comparison, ONS statistics that year attributed around 8,700 deaths directly to alcohol, which on the same basis would therefore equate to around 1.9% of deaths. Whilst these estimates put the relative mortality impacts in context, they take no account of wider social consequences, which are clearly another important area of debate.
The growth in use of e-cigarettes might be considered a large-scale public-health experiment. Despite fears to the contrary, a recent study appeared to show a positive association between the use of e-cigarettes and the success rate of attempts to quit smoking altogether. This comes two-years after a somewhat controversial study by Nutt, et al. (2014), where the harms of e-cigarettes were estimated to be 95% lower than traditional cigarette products. This research was incorporated in the 2015 review by Public Health England. Professor David Nutt, the lead author of the original research, is well known as a neuropsychopharmacologist and expert in harm-reduction. Indeed, it was his criticism of perceived failures to classify narcotics by harm that cost him his UK Government role as chief advisor on drugs in 2009.
Given his prominence in the debate around nicotine products, it is noteworthy that Professor Nutt may take an even more front-line role in a potential harm-reduction strategy for alcohol. Recent press reports have focussed on the possibility of hangover-free synthetic alcohol products, but of course, Nutt's main concern is around the prevention of liver disease and other biological harms. Industry discussion on whether such products could replace traditional alcohol shows this is being taken seriously, and detailed patents have already been applied for.
The kinds of harm-reduction strategy outlined carry potential unforseen risks alonside any predicted rewards. Might the interventions proposed result in problematic outcomes in other ways? For example, there are widely-held concerns that e-cigarettes could make smoking more attractive to children, and it is natural to speculate whether removing hangovers could encourage binge-drinking. Such considerations are valuable, underscoring the fact that harm-reduction techniques applied in the present may lead to unintended consequences in the future.
Setting this aside, however, if initiatives like these find substantial and prolonged take-up, what might happen to mortality rates? Clearly any estimates must consider that not everyone will change their poison. Balanced against that is the fact that the ONS alcohol-related mortality figures previously quoted did not include diseases partially attributable to alcohol, such as cancers of the mouth, oesophagus and liver. Whilst the potential mortality improvements are impossible to quantify accurately, we know from the decline in smoking over the last four decades that mortality rates can and will adjust in line with changing behaviours. If segments of the population shift to using significantly less toxic products, we should expect mortality rates to react accordingly.
References:
Nutt, D.J. et al. (2014) Estimating the Harms of Nicotine-Containing Products Using the MCDA Approach. European Addiction Research. Eur Addict Res 2014;20:218-22 DOI:10.1159/000360220
Beard, E. et al. (2016) Association between electronic cigarette use and changes in quit attempts, success of quit attempts, use of smoking cessation pharmacotherapy, and use of stop smoking services in England: time series analysis of population trends. BMJ 2016; 354 doi: http://dx.doi.org/10.1136/bmj.i4645
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\[y = \frac{\log_e\left(\frac{x}{m}-sa\right)}{r^2}\]
\[\Rightarrow yr^2 = \log_e\left(\frac{x}{m}-sa\right)\]
\[\Rightarrow e^{yr^2} = \frac{x}{m}-sa\]
\[\Rightarrow me^{yr^2} = x-msa\]
\[\Rightarrow me^{rry} = x-mas\]
Signal or noise?
Each year since 2009 the CMI in the UK has released a spreadsheet tool for actuaries to use for mortality projections. I have written about this tool a number of times, including how one might go about setting the long-term rate. The CMI now wants to change how the spreadsheet is calibrated and has proposed the following model in CMI (2016a):
\[\log m_{x,y} = \alpha_x + \beta_x(y-\bar y) + \kappa_y + \gamma_{y-x}\qquad (1)\]
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