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Trinity College Courses
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Caroline Barakat

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HLTC07: Patterns of Health, Disease, and Injury Lecture 11:Demographic and epidemiologic transitions in population health Lecture Outline  Life Expectancy (LE) and longevity o Positive health outcomes today  Survival probability curve o What does it mean?  Gain in life expectancy o Active LE  Actual LE vs. active LE  Aging and longevity o Video refers to the area with the highest # of centenarians living there Life Expectancy (LE)  Average lifespan of a cohort of individuals observed after an inception of time  Not really looking at how long each person is going to live; looking @ a collection of individuals who are observed after some point in time (inception time) – could be different – so can look @ life expectancy of people living with cancer, life expectancy after certain treatment, general life expectancy after birth o There are diff inception times: birth, onset/diagnosis of certain disease/diff types of cancer, initiation of treatment  From theoretical perspective, calculating life expectancy involves observations in which all members of a certain cohort have died – that’s the only way you can know how much time they have contributed – average life expectancy  Total amount of time that people in cohort lived and then dividing by the # of individuals at the inception point o Formula = simple – average o How many months/years every cohort member has lived summed/# of cohort members Calculate Life Expectancy  Graph shows: months since inception point (diagnosis or treatment – not general @ birth life expectancy because people longer than just months) o Life expectancy = 130/10 = 13 months  So, follow cohort until they die and add up what the contributed divided by the # of members in cohort  LE = Total time/# of individuals in a cohort  Graph shows horizontal life spans for each individual (in months)  But usually, when we have a large # of individuals we look at it as vertical cohort memberships/bars o So, in terms of how many individuals contributed 1 month, how many individuals contributed 2 months etc; o When looking @ vertical bars, as members of the cohort die, the vertical bars shrink in size o At inception point, right @ zero, we had 684 cohort members  They all contributed one month and up to 6 months, beyond that, ppl started dying – so have vertical bars that give an indication of how many individuals contributed to the life expectancy and because you have large # of individuals – can’t have horizontal bars – use vertical bars instead  What represents the total number of life-months experienced by the cohort? ... N=684  What gives an indication of life expectancy/total contribution of each of those cases? o Integration – area under the curve o Larger the area – have big contribution – that’s the total # of months; less area = less months contributing – then you still have to divide it by 684 o Area under curve = total # of months all individuals in the cohort contributed in terms of survival  LE = area under the curve/684 o Area under the curve aka survival curve because gives indication of how many people survive beyond a certain month  You are told that area = 8236  Tied into the concept of survival curve is the concept of survival probability curve The Survival Probability Curve  Proportion of a population living after inception point  Y-axis here is no longer the # of cases but rather you have a scale of 0-1 o At 0 point, everyone survives o Reason why it’s called survival probability curve is because now you have y-axis on probability scale 0-1 o How many people will survive until 20 months?  Go 2 20 months – see that there’s 20% survival of 20 months  Survival vs. probability to which people live to certain time o Gives indication of proportion of a population living after a given age/treatment etc; o How much time a person survives after an inception point versus what is the survival rate  Main difference between survival curve and survival probability curve is the latter is on a probability scale of 0- 1 whereas the former gives indication of how many individuals are part of the cohort and then the total amount of time that they all contributed to LE Gain in Life Expectancy  Usually used when there’s an association or one wants to associate with one health strategy over another so that you can see what works better for those individuals  You have time since inception and then you have 2 scenarios: o Treatment and control o Or maybe two treatments  You’re following the cohort again (same as b4) – build survival curve first and then build survival probability curve o This gives an indication of those 2 curves o Control has lighter shade of grey survival probability curve whereas the treatment = darker shade o Can determine if someone should go for that particular treatment – can see that it has some effect over the control; has been shifted to the right o Benefit of the treatment is the difference – it is the area in the middle which is referred to as delta LE (aka the change in LE) o So, the difference in benefit = the area between those 2 curves which represents whatever life expectancy was after treatment minus that of control o Is it beneficial for the treatment to be given? Look @ area between the 2 curves Active Life Expectancy  Not looking at mortality or morbidity – looking @ healthy measures – how long people would live but also looking @ good quality of life  Proportion of total life expectancy to be lived without chronic disability (not going thru disability that doesn’t allow you to live an active life)  Graph: UN 2007, World population prospects o Gives indication of active life expectancy and life expectancy for 3 diff time periods (1935, 1999, 2008) o First one is solid bar – active life expectancy in 1935 vs. another line for LE for 1935 – that difference = amount of time that’s contributing to LE (=living with disability; it is not o active life expectancy) o Same thing again: active LE in 1999 and total LE in 1999 – also see a gap – gap is attributed to many things: difference between active life expectancy and actual life expectancy = people are living longer but necessarily without disability – so older people aren’t living active lifestyle but they’re also living longer – pushes LE over and above but not active life expectancy o Implication is that by 2080 – we have changing trend of increased LE and also hoping that there’s smaller gap between active LE and LE o This is all very relevant for countries looking @ retirement options, increasing retirement rate – ensure balance in society o European – retirement rate from 65 to 67 to achieve balance Demography of Aging  According 2 epidemiological transition theory, there are many things behind the impro
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