Tag Archives: Life expectancy

Ever Increasing Life Expectancies Come to an Abrupt End Among American Whites

Big discontinuities are fascinating. Just when we think we understand something, the trend line changes radically. Examples of unexpected discontinuities in trends include the massive decline in smoking among African-Americans in the 1980s [1]; the drop in crime in high-income cities over the last decade or so [2]; and the recent drop in teenage pregnancy rates.[3] These are favourable trends in contrast to the sudden end of year on year decline in mortality among the majority population in one large country – white people in the US.[4] Anne Case and Angus Deaton drill down into the numbers in their recent paper:

  1. Is this trend confined to white people? Yes, black and Hispanic people continue to experience declining mortality rates.
  2. Is this trend seen in other high-income countries? No – in France, Sweden, Japan and the UK, age-specific mortality continues to decline across the populations.
  3. How does it differ among whites by economic class? Using education as a proxy, a decline in life expectancy is confined to those with no education beyond high-school.
  4. What diseases are driving it? ‘Deaths of despair’ (suicide, alcoholic cirrhosis, drug overdose) are rising among white people in the US in absolute terms, and in comparison with non-white groups and with other countries. Cardiovascular deaths are no longer declining among whites in the US, even as they continue to do so in other countries. Increases in ‘deaths of despair’ along with arrest in declining cardiovascular diseases, combine to extinguish the declining trend.
  5. Is the phenomenon localised geographically? No, the ‘epidemic’ in ‘deaths of despair’ among white people covers rural and urban areas, and has pretty much become country-wide.
  6. Is the problem gender specific? No, the rise in ‘deaths of despair’ among the less-educated group affects both women and men.
  7. What are the long term trends? While the differences in mortality between better and less well educated groups are getting narrower in Europe, the gap is getting wider among whites in the US. This widening gap is also reflected in changes in self-assessed health.

So is all this really just a reflection of widening economic disparities? No:

  1. Disparities are widening within the black community and between black people and white people. However, mortality is converging between rich and poor black and Hispanic people, and ‘deaths of despair’ are not increasing in these ethnic groups.
  2. Widening disparities are seen in all comparator countries – in Spain, ‘deaths of despair’ actually declined through a vicious economic downturn between 2007 and 2011, for example.
  3. The difference in outcome correlates much more strongly with change in education than change in income.
  4. Historically there are many instances when mortality and inequality have moved in different directions, and selective reporting can be used by unscrupulous ideologues to buttress either side of this argument.

So why has it happened. Here we need to turn to sociology (in some desperation). A novel, called ‘Fishtown’ (by Neal Goldstein) captures some of the sociology; a tale of a rising feeling of purposelessness as workers overseas and machines at home combine to force less educated people (men especially) out of jobs. Such people rely on welfare, while immigrants take over the lowest paid jobs. Another explanation turns on the idea of differentials – this time between whites and non-whites, and loss of status rather than failure to achieve it – “if you have always been privileged, equality begins to look like oppression.” Case and Deaton are careful to point out that the above explanations are not strongly supported by the data. But there is something ‘out there’ – a ‘latent variable’ with a long memory (i.e. operating over the life course of various ‘cohorts’ of people). Many commentators pretend they have understood these latent variables, but I think we are going to have to look a lot harder and resist the beguiling but facile explanations offered up by journalists, political commentators, and academics alike (a point pursued in the next exciting instalment of your News Blog).— Richard Lilford, CLAHRC WM Director

References:

  1. Oredein T & Foulds J. Causes of the Decline in Cigarette Smoking Among African American Youths From the 1970s to the 1990s. Am J Public Health. 2011; 101(10): e4-14.
  2. The Economist. Falling crime. Where have all the burglars gone? The Economist. 20 July 2013.
  3. Wellings K, Palmer MJ, Geary RS, et al. Changes in Conceptions in Women Younger Than 18 Years and the Circumstances of Young Mothers in England in 2000-12: an Observational Study. Lancet. 2016; 388: 586-95.
  4. Case A, & Deaton A. Mortality and morbidity in the 21st century. Brookings Papers on Economic Activity. BPEA Conference Drafts. March 23-24, 2017.
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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

References:

  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.

Decreasing Inequality in Mortality among American Children

In the previous blog we presented evidence of current life expectancies by income in the US, the neighbourhood effect of relative wealth, and the relative effect of relative wealth by income quartile. Life expectancy was strongly correlated with income. But this was life expectancy at age 40. A recent paper in Science [1] finds that, among children, mortality is improving faster among poor than rich households, especially in poor parts of the country. This is happening despite no reductions in income inequality over the observation period. The reasons are not clear, but there have been substantial public health programmes for children over the last 25 years, along with improved access to care in the US. These may be helping improve outcomes for poor children while marginal gains for richer children are becoming increasingly difficult to achieve.

— Richard Lilford, CLAHRC WM Director

Reference:

  1. Currie J, Schwandt H. Inequality in mortality decreased among the young while increasing for older adults, 1990-2010. Science. 2016; 352(6286): 708-12.

Relative Wealth and Health

It has been known since the time of Condorcet, over 200 years ago, that poverty is bad for you – an income effect.[1] Studies have also shown an association between relative poverty and life expectancy – a relative income effect.[2] It has become common to interpret these associations as evidence that relative poverty causes poor health, net of absolute wealth. Previous studies are trumped by the largest association study ever conducted, based on nearly 1.4 trillion person years of observation and 1.4 billion de-identified tax records across the United States of America.[3] Beat that! So, what did it find?

  1. The association between wealth and longevity (the income effect) is confirmed. The difference in male life expectancy at age 40 differs by a colossal 15 years between the top and bottom 1% on the income scale. Interestingly, there is no threshold above which the association fades; rather the reverse.
  2. Inequality has increased in recent years because life expectancy has increased faster among people on high incomes than among those on low incomes.
  3. Differences in mortality adjusted for race and ethnicity and net of income varies by geographic area. In other words, the differences in life expectancy between rich and poor itself differs by geographic area.
  4. Differences in income effect by area are mostly explained by differences in health behaviour (rather than, for example, access to healthcare).

The finding that different areas have very different survival rates net of income, allows the effect of numerous other variables on the income effects (absolute and relative) to be explored. Some places have large gradients in wealth, others smaller. The existence of a relative income effect is confirmed by a negative correlation between inequality (measured by the Gini co-efficient) and longevity. But this is an artefact of the concave nature of the relationship between income and life expectancy. In fact, among the lowest quartile by income there is no correlation between wealth disparity and health, whereas, ironically, it is strongest among the upper quartile. So the idea that it is the poor who exhibit the strongest relative income effect is completely wrong. In fact, poor people have healthier behaviours and live longer when they live in rich cities alongside a highly-educated, high income populations than in poorer cities with ‘better’ Gini co-efficients. Perhaps this is because rich cities can raise more in tax at a given tax rate. Slum formation is more rapid in cities with a high Gini co-efficient. This is sometimes interpreted as a high Gini causing poverty, rather than the more plausible interpretation that rational internal migrants gravitate to richer cities. These data are important because they call into doubt the simplistic idea that all we have to do to right the world’s wrongs is to tax rich people more heavily.

— Richard Lilford, CLAHRC WM Director

References:

  1. Benzeval M, Bond L, Campbell M, et al. How Does Money Influence Health? York: Joseph Rowntree Foundation. 2014.
  2. Wilkinson RG, Pickett KE. Income inequality and population health: a review and explanation of the evidence. Soc Sci Med. 2006; 62(7): 1768-84.
  3. 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.

Who Does Better with Respect to Health: the Winner of High Political Office or the Gallant Loser?

Every so often the CLAHRC WM Director reads an article and says, “By Jupiter, why didn’t I think of doing that?” Such an article compared the life expectations of the winners and runners up of 297 national elections across 17 countries.[1] From the Whitehall studies one might have concluded that the winner takes all – the feel-good factor of being a winner in life’s race would presage a longer life-span. Not so. The winners on average live a full 2.7 years less than the losers – a larger effect size than many of the unhealthy behaviours we tackle in public health. US Presidents are known to have the same life expectancy as the general US population, but given their high social class they should live longer. So the stress of high office really might be bad for health. The CLAHRC WM Director posits an inverted U-shaped stress curve:

048 DC - Who Does Better with Respect to Health - Fig 1

— Richard Lilford, CLAHRC WM Director

Reference:

  1. Olenski AR, Abola MV, Jena AB. Do heads of government age more quickly? Observational study comparing mortality between elected leaders and runners-up in national elections of 17 countries. BMJ. 2015; 351: h6424.

An Extraordinary Collaborative Effort: Disability Adjusted Life Years (DALYs) for 306 Countries

Last fortnight’s blog post featured a synopsis of the mighty Global Burden of Disease Study on life years lost across different countries and across the world over time due to different diseases. A subsequent paper has recently been published in the Lancet that uses a standardised framework and method to estimate summary measures of health loss expressed as DALYs and Health Life Expectancy.[1] Health loss was estimated for 306 diseases/conditions across 188 countries at regular intervals between 1990 and 2013. Headline messages from this 46 page study are as follows:

  1. Worldwide life expectancy rose by 6.2 years (from 65 to 71 years) over the 23 years of the study.
  2. Age-standardised DALYs fell by a mighty 27%.
  3. Indicators (total DALYs and age-specific DALYs) improved dramatically for communicable diseases; and for maternal, neonatal and nutritional diseases.
  4. Age-adjusted DALYs have also declined for non-communicable disease – a surprise?
  5. Some communicable diseases (notably leishmaniasis and dengue) bucked the trend for communicable diseases as a whole and registered a recent increase in DALYs. The CLAHRC WM Director questions the finding regarding leishmaniasis – he suspects that visceral leishmaniasis, at least, is declining.
  6. The greatest causes of DALYs were ischaemic heart disease, pneumonia, stroke, spinal pain, and road injuries. The CLAHRC WM Director suspects that mental illness is underestimated in this study?
  7. Leading causes of DALYs are highly variable across countries.
  8. Progress has been most rapid in the latter part of the survey period thanks to major reductions in HIV/AIDS and malaria, along with maternal, neonatal and nutritional disorders.
  9. DALY rates for neoplasms and cardiovascular disease are minimally related to socio-economic status except that risk for cardiovascular disease drops at the very highest economic level.

The health of the world’s population really has improved and to quite a dramatic degree. This has happened even in countries that have not prospered economically. The GBD study is a remarkable achievement, up there with the human genome project. It shows that science is a massive public good, and is a stark vindication of Enlightenment values and investment in research.

— Richard Lilford, CLAHRC WM Director

Reference:

  1. GBD 2013 DALYs and HALE Collaborators. Global, regional, and national disability-adjusted life years (DALYs) for 306 diseases and injuries and health life expectancy (HALE) for 188 countries, 1990–2013: quantifying the epidemiological transition. Lancet. 2015; 386: 2287-323.

Life expectancy of high-risk populations

Appleby’s recent paper in the BMJ [1] shows steadily worsening health across social care bands, with no particular step change at the lower end of the distribution. However, there are some particularly vulnerable small populations in most countries. In the UK the homeless form such a group, with an average age of death of 47 years, some 30 years less than the general population.[2] We thought it would be interesting to compare the life expectancy of particular high-risk populations with those of the host community across a range of middle- and high-risk countries. The results are shown in Table 1. The sharpest gradient applies to residents of the largest informal settlement in Kenya, who have a terrible life expectancy of about half the national mean.[3] [4] The Inuit population of Canada have a life expectancy that is 84% of the national mean,[5] while the Aboriginal and Torres Strait people of Australia have a life expectancy that is 88% of the Australian average.[6] The indigenous peoples of the Yunnan province in China,[7] the Alaska Natives of the USA,[8] the Māori people of New Zealand,[9] and the Pacific Islanders of the USA [10] all fare relatively better. Table 2 shows that excess mortality across at least one of these groups is attributed to a wide range of medical conditions.[11] As to the mechanism linking social conditions to these diseases, we would be interested in your views. In the meantime, we note that none of the above groups, save the children of Kibera slum, have an outcome as bad as that of the homeless in the UK.

Table 1. Life expectancy of indigenous populations compared to that of the country/state as a whole.

Kibera slum, Kenya – 30 year life expectancy compared to 63.5 years for country [3, 4]; Inuit people, Canada – 66.9 year life expectancy compared to 79.5 years for country [5]; Aboriginal & Torres Strait Islanders, Australia – 71.4 years life expectancy compared to 81.2 years for country [6]; Yunnan indigenous population, China – 64.5 years life expectancy compared to 71.4 years for country [7]; Alaska Natives, USA – 70.5 years life expectancy compared to 77.3 years for state [8]; Māori people, New Zealand – 74.7 years life expectancy compared to 81.2 years for country [9]; Native Hawaiians & other Pacific Islanders, USA – 74.3 years life expectancy compared to 80.5 for state.[10]

NB. All life expectancies, except for Native Hawaiians & other Pacific Islanders, are an average of the male and female life expectancies.

Table 2. Causes of excess mortality among Aboriginal and Torres Strait Islanders in Australia (2004-2008).[11]

SMR for death by diseases of circulatory system is 3.0 in males (accounting for 26.3% of excess deaths), and 1.8 in females (accounting for 24.4% of excess deaths); SMR for death by diseases of digestive system is 6.4 in males (accounting for 7.3% of excess deaths), and 4.2 in females (accounting for 9.8% of excess deaths); SMR for death by diseases of respiratory system is 3.9 in males (accounting for 8.6% of excess deaths), and 2.3 in females (accounting for 9.3% of excess deaths); SMR for death by endocrine, metabolic & nutritional disorders is 7.2 in males (accounting for 9.1% of excess deaths), and 6.4 in females (accounting for 17.6% of excess deaths); SMR for death by external causes is 3.7 in males (accounting for 20.2% of excess deaths), and 1.5 in females (accounting for 7.3% of excess deaths); SMR for death by neoplasms is 1.7 in males (accounting for 9.9% of excess deaths), and 1.3 in females (accounting for 8.8% of excess deaths).

* Standardised Mortality Ratio – observed deaths as a proportion of deaths expected based on age, gender and cause-specific rates for non-Indigenous population.
† Excess deaths (observed deaths minus expected deaths), as a percentage of total excess deaths for all causes.

— Richard Lilford, CLAHRC WM Director

— Peter Chilton, Research Associate

References:

  1. Appleby J. Health related lifestyles of children: getting better? BMJ. 2014; 348: g3025.
  2. Crisis. Homelessness: A silent killer. A research briefing on mortality among homeless people. 2011. [Online].
  3. The Kibera Law Centre. Facts. 2014. [Online].
  4. Central Intelligence Agency. The World Factbook – Kenya. 2014. [Online].
  5. Wilkins R, Uppal S, Finés P, Senéchal S, Guimond É, Dion R. Life expectancy in the Inuit-inhabited areas of Canada, 1989-2003. Statistics Canada Health Reports. 2008. [Online].
  6. Australian Bureau of Statistics. Life tables for Aboriginal and Torres Strait Islander Australians. Report No. 3302.0.55.003. Canberra: ABS; 2013.
  7. Li J, Luo C, De Klerk N. Trends in infant/child mortality and life expectancy in Indigenous populations in Yunnan Province, China. Aust NZ J Publ Heal. 2008; 32(3): 216-23.
  8. Hunsinger E. Alaska Populations Projections 2007-2030. Alaska Native Population Changes Workshop; 2008 April. [Online].
  9. Statistics New Zealand. Period life tables. 2013. [Online].
  10. Look MA, Trask-Batti MK, Agres R, Mau ML, Kaholokula JK. Assessment and Priorities for Health & Well-being in Native Hawaiians & Other Pacific Peoples. Honolulu, HI: Centre for Native and Pacific Health Disparities Research; 2013.
  11. Australian Institute of Health and Welfare. Life expectancy and mortality of Aboriginal and Torres Strait Islander people.Cat. No. IHW 51. Canberra: AIHW; 2011.