A question rolled into my inbox from a senior clinician I know.
“Do you have a few killer facts about:
• Prevalence of LTCs, trends over last 5 years and predictions for next 5 years?
• Ditto prevalence of multi morbidity
• Ditto healthy life expectancy..
We have all sorts of conflicting stories in our place, so I though I’d ask an expert!”
I get asked similar from time to time, hence the blogged answer so as others can not reinvent wheels.
I’m def not an expert. Just a jobbing public health specialist
There’s no simple pithy answer
You definitely need to get yourself a public health dept
The below is a bit fag packet and I was doing whilst watching the telly last night
I wont get time to tidy it soon (or ever), but if you can draw out some killer facts from it all the better
I’ll also phone a friend. Thanks to Toni for wisdom
2). On simple LTC epi
QOF is obvious place to look.
We can track QOF nationally very easily www.gpcontract.co.uk I have just looked at NHSE stats for diabetes as an example and the recorded prevalence has gone up every year for the last 7 years that I looked at (2011 – 4.5% up to 2017 – 5.4%)
This is obviously easy data to pull together and present, but it needs to be contextualised alongside the epi studies/survey because QOF is about diagnosis rather than actual prevalence.
Any drives to better diagnose people such as the recent dementia work would probably show quite a dramatic increase in the QOF recorded prevalence of dementia, whereas this wont all be due to more people with dementia, but in part due to us being better at diagnosing and recording. Locally we have done similar things with diabetes…so the interpretation and narrative is important.
It is what it is, use advisedly. It’s a fairly mediocre proxy for true prevalence, tells you nothing about incidence and says as much about diagnostic tendencies as there is about disease.
It’s administrative data, I’ve written in the past on the flaws and issues of pretending administrative data is good for epi.
Stick with the published epidemiological studies and the national health surveys for the big areas such as CVD, cancer, dementia, diabetes, mental health.
On specific forecasts of LTCs
tricky and definitely the space of proper epi analysis
4 papers I often use are
· Cancer incidence rates are projected to decrease by 0.03% in males and increase by 0.11% in females yearly between 2015 and 2035;
· thyroid, liver, oral and kidney cancer are among the fastest accelerating cancers.
· 243 690 female and 270 261 male cancer cases are projected for 2035.
· Breast and prostate cancers are projected to be the most common cancers among females and males, respectively in 2035.
· Most cancers’ mortality rate is decreasing; there are notable increases for liver, oral and anal cancer.
Dementia – Abhari – Age specific dementia incidence is declining. The number of people with dementia in England and Wales is likely to increase by 57% from 2016 to 2040. This increase is mainly driven by improved life expectancy.
Lancet paper on liver disease projections
Hypertension is also one to watch I think alongside diabetes (incidence preferably but we’ll take prevalence) – these to me are “key markers” for problems down track
Don’t discount alcohol and drug abuse – the numbers may be small relative to above but there is evidence that the adverse impact on people’s lives is increasing and worryingly so (throughout the life course
This is a job you’ve got to do disease by disease
There’s other stuff out there. Some a bit ropy, some quite good….
2) epi of multi morbidity
v little if any forward projection
English Longitudinal Study of Ageing may have some good stuff
See for eg here – random pick.
We used data on 15,688 core participants from six waves of the English Longitudinal Study of Ageing, with complete information on physical activity. Self-reported physical activity was categorised as inactive, mild, moderate and vigorous levels of physical activity. We calculated the number of morbidities and the prevalence of multimorbidity (more than 2 chronic conditions) between 2002 and 2013 overall and by levels of self-reported physical activity. We estimated the odds ratio (OR) and 95 % confidence intervals (CI) for multimorbidity by each category of physical activity, adjusting for potential confounders.
There was a progressive decrease over time in the proportion of participants without any chronic conditions (33.9 % in 2002/2003 vs. 26.8 % in 2012/2013). In contrast, the prevalence of multimorbidity steadily increased over time (31.7 % in 2002/2003 vs. 43.1 % in 2012/2013). Compared to the physically inactive group, the OR for multimorbidity was 0.84 (95 % CI 0.78 to 0.91) in mild, 0.61 (95 % CI 0.56 to 0.66) in moderate and 0.45 (95 % CI 0.41 to 0.49) in the vigorous physical activity group.
You’d need to dig into the details and the data to get a real sense
b) Frailty and use of EFI as a proxy
May be some stuff in the EFI research stable that gives trends in EFI as a proxy?
Andy Clegg or John Young best.
I’ve not checked
Castillo et al is a decent place to look
· Between 2015 and 2025, the number of people aged 65 years and older will increase by 19·4%, from 10·4 million to 12·4 million.
· The number living with disability will increase by 25·0% (95% UI 21·3–28·2), from 2·25 million (2·24–2·27 million) to 2·81 million.
· The age-standardised prevalence of disability among this population will remain constant, at 21·7% in 2015 and 21·6% in 2025.
· Total life expectancy at age 65 years will increase by 1·7 years, from 20·1 years to 21·8 years (20·2–23·6).
· Disability-free life expectancy at age 65 years will increase by 1·0 years (95% UI 0·1–1·9), from 15·4 years to 16·4 years )
· life expectancy with disability will increase more in relative terms, with an increase of roughly 15% from 2015 to 2025
· The number of older people with care needs will expand by 25% by 2025, mainly reflecting population ageing rather than an increase in prevalence of disability. Lifespans will increase further in the next decade, but a quarter of life expectancy at age 65 years will involve disability.
it’s a modelling study. It gets in to the tricky territory of growing pop, stratified by age and then applies morbidity estimates and how they may play out.
I once had and effort to describe the epi of MM
it’s here. There is little to no longitudinal data.
I had an email chat with Prof Guthrie, the author of the benchmark Barnet et al study a year or so ago re londitudinal data, no longitudinal data.
See middle of blog for key points
Bradford have been looking at time trend of multi morbidity – this is tricky. They have been looking at this locally, but only have data over a 3 year time period. So far our findings are identical – same prevalence of multimorbidity in 2017 as in 2014. Also the age at which people ‘develop’ multimorbidity is the same. They also recognise the need to model this forward given the multimorbidity trumps age.
Yorkshire Health Study might be worth a look, but don’t think any longitudinal data.
f). Hold the press. Kington et al published this in early 2018
Kingston estimated the prevalence of, numbers with, and years lived with, chronic diseases, geriatric conditions and multi-morbidity. Between 2015 and 2035, multi-morbidity prevalence is estimated to increase, the proportion with 4+ diseases almost doubling (2015:9.8%; 2035:17.0%) and two-thirds of those with 4+ diseases will have mental ill-health (dementia, depression, cognitive impairment no dementia).
Multi-morbidity prevalence in incoming cohorts aged 65–74 years will rise (2015:45.7%; 2035:52.8%). Life expectancy gains (men 3.6 years, women: 2.9 years) will be spent mostly with 4+ diseases (men: 2.4 years, 65.9%; women: 2.5 years, 85.2%), resulting from increased prevalence of rather than longer survival with multi-morbidity.
The paper advocates for a new focus on prevention of, and appropriate and efficient service provision for those with, complex multi-morbidity
Multimorbidity patterns in the elderly: a prospective cohort study
“We identified a very large proportion of people over 65 years with multimorbidity, distributed in six clusters; five affected a specific system in the body and one had a nonspecific pattern. The major portion of the sample fit this last pattern, which had few diseases; this finding could be related to genetic or social characteristics of the sample. On the other hand, stability in a specific pattern over an extended time period might give us the information needed to take a new approach and improve a patient’s situation. For instance, a new clinical practice guideline could be developed to control a combination of chronic diseases rather than each one individually.
As the prevalence of chronic diseases was stable over the period studied, multimorbidity patterns also became firmer. Therefore, the k-means technique is useful to analyse multimorbidity patterns in real-world data.
The observation that multimorbidity patterns are constant over time is very useful for the specific clinical management of each patient who fits a specific multimorbidity pattern. Further studies using this method in other groups of patients should be performed to validate the results obtained.”
See this one from RAND. Trends in multimorbidity in US.
(Yet) Another study showing relatively static prevalence of multimorbidity over 6 year period. Consistent with other studies + our local figures in 2 towns that I know of (over 3 yr period)
3) HLE and LE
ONS is best place to look for the definitive at source. Also very unbiased. Its often a bit impenetrable
See here re disability free LE as a proxy for both HLE and MM trends
See here. Note section 6 if this, they ducked the trend over time issue
PHOF is more accessible but don’t think there’s national view.
My effort to describe why the HLE story is as it is in the first few para of this blog on my DPH report this year
· combo of our past lifestyle catching up,
· ditto previous social policy from 70s and especially deindustrialisation of the 80s now catching up with people.
· Austerity and (huge) hollowing out of the social welfare safety net (across lots of service and policy areas) is basically the fuel to throw on the fire
this blog is in my view the critical place to look re commentary on trends in LE & HLE here
Note – figure 3 – the flattening of the curve since 2011 or so. This is the bit that’s got everyone worried.
There was a rise in mortality in 2015 for the first time in decades.
Although fewer deaths were registered in 2016, there hasn’t been any statistically significant improvement in mortality rates since 2011.
For me that’s the key message.
Lots of people trying to understand this – some of the studies that I have seen have political element.
Recent KF blog is also good: https://www.kingsfund.org.uk/blog/2017/11/improvements-mortality-slowed-down
We know enough about is whether this trend holds true for all population groups, or is the overall data masking trends. There are some differences between mean and women.
Bradford and Sheffield have done some work on this, esp deprivation. Some indications that more affluent areas have still seen improvements in mortality rates, whilst more deprived areas have not. Not stat significant at local level, hence the need for national analysis
This is helpful, perhaps a little over focus on the noted changes in mortality since 2015
No smoking gun here. Measured analysis
So no killer facts there
Needs a little more work. There is a lot of information out there which needs pulling together in a coherent story.
Different information sources provide different contexts
Above is best I can do between jobs and whilst watching Eastenders.
Get yourself a public health dept.