--- imach/html/doc/imach.htm 2004/06/16 12:05:30 1.1 +++ imach/html/doc/imach.htm 2004/06/16 22:45:51 1.3 @@ -1,12 +1,10 @@ - + - Computing Health Expectancies using IMaCh - @@ -36,7 +34,7 @@ color="#00006A">INEDEUROREVES

Version -0.8a, May 2002

+0.97, June 2004


@@ -102,29 +100,30 @@ population) is then decomposed into DFLE computing HE is usually called the Sullivan method (from the name of the author who first described it).

-

Age-specific proportions of people disable are very difficult -to forecast because each proportion corresponds to historical -conditions of the cohort and it is the result of the historical -flows from entering disability and recovering in the past until -today. The age-specific intensities (or incidence rates) of -entering disability or recovering a good health, are reflecting -actual conditions and therefore can be used at each age to -forecast the future of this cohort. For example if a country is -improving its technology of prosthesis, the incidence of -recovering the ability to walk will be higher at each (old) age, -but the prevalence of disability will only slightly reflect an -improve because the prevalence is mostly affected by the history -of the cohort and not by recent period effects. To measure the -period improvement we have to simulate the future of a cohort of -new-borns entering or leaving at each age the disability state or -dying according to the incidence rates measured today on -different cohorts. The proportion of people disabled at each age -in this simulated cohort will be much lower (using the exemple of -an improvement) that the proportions observed at each age in a -cross-sectional survey. This new prevalence curve introduced in a -life table will give a much more actual and realistic HE level -than the Sullivan method which mostly measured the History of -health conditions in this country.

+

Age-specific proportions of people disabled (prevalence of +disability) are dependent on the historical flows from entering +disability and recovering in the past until today. The age-specific +forces (or incidence rates), estimated over a recent period of time +(like for period forces of mortality), of entering disability or +recovering a good health, are reflecting current conditions and +therefore can be used at each age to forecast the future of this +cohortif nothing changes in the future, i.e to forecast the +prevalence of disability of each cohort. Our finding (2) is that the period +prevalence of disability (computed from period incidences) is lower +than the cross-sectional prevalence. For example if a country is +improving its technology of prosthesis, the incidence of recovering +the ability to walk will be higher at each (old) age, but the +prevalence of disability will only slightly reflect an improve because +the prevalence is mostly affected by the history of the cohort and not +by recent period effects. To measure the period improvement we have to +simulate the future of a cohort of new-borns entering or leaving at +each age the disability state or dying according to the incidence +rates measured today on different cohorts. The proportion of people +disabled at each age in this simulated cohort will be much lower that +the proportions observed at each age in a cross-sectional survey. This +new prevalence curve introduced in a life table will give a more +realistic HE level than the Sullivan method which mostly measured the +History of health conditions in this country.

Therefore, the main question is how to measure incidence rates from cross-longitudinal surveys? This is the goal of the IMaCH @@ -196,6 +195,9 @@ Unix.

(1) Laditka, Sarah B. and Wolf, Douglas A. (1998), "New Methods for Analyzing Active Life Expectancy". Journal of Aging and Health. Vol 10, No. 2.

+

(2) Lièvre A., Brouard N. and Heathcote Ch. (2003) Estimating Health Expectancies +from Cross-longitudinal surveys. Mathematical Population Studies.- 10(4), pp. 211-248


@@ -223,19 +225,30 @@ survival time after the last interview.<

The data file

In this example, 8,000 people have been interviewed in a -cross-longitudinal survey of 4 waves (1984, 1986, 1988, 1990). -Some people missed 1, 2 or 3 interviews. Health statuses are -healthy (1) and disable (2). The survey is not a real one. It is -a simulation of the American Longitudinal Survey on Aging. The -disability state is defined if the individual missed one of four -ADL (Activity of daily living, like bathing, eating, walking). -Therefore, even is the individuals interviewed in the sample are -virtual, the information brought with this sample is close to the -situation of the United States. Sex is not recorded is this -sample.

+cross-longitudinal survey of 4 waves (1984, 1986, 1988, 1990). Some +people missed 1, 2 or 3 interviews. Health statuses are healthy (1) +and disable (2). The survey is not a real one. It is a simulation of +the American Longitudinal Survey on Aging. The disability state is +defined if the individual missed one of four ADL (Activity of daily +living, like bathing, eating, walking). Therefore, even if the +individuals interviewed in the sample are virtual, the information +brought with this sample is close to the situation of the United +States. Sex is not recorded is this sample. The LSOA survey is biased +in the sense that people living in an institution were not surveyed at +first pass in 1984. Thus the prevalence of disability in 1984 is +biased downwards at old ages. But when people left their household to +an institution, they have been surveyed in their institution in 1986, +1988 or 1990. Thus incidences are not biased. But cross-sectional +prevalences of disability at old ages are thus artificially increasing +in 1986, 1988 and 1990 because of a higher weight of people +institutionalized in the sample. Our article shows the +opposite: the period prevalence is lower at old ages than the +adjusted cross-sectional prevalence proving important current progress +against disability.

Each line of the data set (named data1.txt -in this first example) is an individual record which fields are:

+in this first example) is an individual record. Fields are separated +by blanks:

-
  • ncovcol=2 Number of covariate columns in the - datafile which precede the date of birth. Here you can - put variables that won't necessary be used during the +
  • ncovcol=2 Number of covariate columns included in the + datafile before the column of the date of birth. You can have +covariates that won't necessary be used during the run. It is not the number of covariates that will be - specified by the model. The 'model' syntax describe the - covariates to take into account.
  • + specified by the model. The 'model' syntax describes the + covariates to be taken into account during the run.
  • nlstate=2 Number of non-absorbing (alive) states. Here we have two alive states: disability-free is coded 1 and disability is coded 2.
  • @@ -343,7 +356,24 @@ line
  • If mle=1 the program does the maximisation and the calculation of health expectancies
  • If mle=0 the program only does the calculation of - the health expectancies.
  • + the health expectancies and other indices and graphs +but without the maximization.. + There also other possible values: +
  • weight=0 Possibility to add weights.

    Once the running is finished, the program -requires a caracter:

    +requires a character:

    @@ -1246,6 +1429,10 @@ requires a caracter:

    +In order to have an idea of the time needed to reach convergence, +IMaCh gives an estimation if the convergence needs 10, 20 or 30 +iterations. It might be useful. +

    First you should enter e to edit the master file mypar.htm.

    @@ -1254,9 +1441,9 @@ edit the master file mypar.htm. <
    - Copy of the parameter file: ormypar.txt
    - Gnuplot file name: mypar.gp.txt
    - - Observed prevalence in each state: prmypar.txt
    - - Stationary prevalence in each state: plrmypar.txt
    - Transition probabilities: pijrmypar.txt
    - Life expectancies by age and initial health status @@ -1270,7 +1457,7 @@ edit the master file mypar.htm. < health status (estepm=24 months): vrmypar.txt
    - Health expectancies with their variances: trmypar.txt
    - - Standard deviation of stationary prevalences: vplrmypar.txt
    No population forecast: popforecast = 0 (instead of 1) or stepm = 24 (instead of 1) or model=. (instead of .)
    @@ -1281,10 +1468,10 @@ edit the master file mypar.htm. < -One-step transition probabilities
    -Convergence to the - stationary prevalence
    - -Observed and stationary + period prevalence
    + -Cross-sectional and period prevalence in state (1) with the confident interval
    - -Observed and stationary + -Cross-sectional and period prevalence in state (2) with the confident interval
    -Health life expectancies by age and initial health state (1)
    @@ -1304,7 +1491,7 @@ simple justification (name, email, insti href="mailto:brouard@ined.fr">mailto:brouard@ined.fr and mailto:lievre@ined.fr .

    -

    Latest version (0.8a of May 2002) can be accessed at Latest version (0.97b of June 2004) can be accessed at http://euroreves.ined.fr/imach