12 0.0 0.0 @@ -411,7 +409,7 @@ Each of the four lines starts with indicGuess values for computing variances
This is an output if mle=1. But it can be -used as an input to get the vairous output data files (Health +used as an input to get the various output data files (Health expectancies, stationary prevalence etc.) and figures without rerunning the rather long maximisation phase (mle=0).
@@ -442,7 +440,7 @@ consists in indices "ij" folloCovariance matrix of parameters
This is an output if mle=1. But it can be -used as an input to get the vairous output data files (Health +used as an input to get the various output data files (Health expectancies, stationary prevalence etc.) and figures without rerunning the rather long maximisation phase (mle=0).
@@ -477,23 +475,24 @@ covariances between aij and bij: -last -uncommented line
+Age range for calculation of stationary +prevalences and health expectancies
agemin=70 agemax=100 bage=50 fage=100Once we obtained the estimated parameters, the program is able to calculated stationary prevalence, transitions probabilities -and life expectancies at any age. Choice of age ranges is useful +and life expectancies at any age. Choice of age range is useful for extrapolation. In our data file, ages varies from age 70 to -102. Setting bage=50 and fage=100, makes the program computing -life expectancy from age bage to age fage. As we use a model, we -can compute life expectancy on a wider age range than the age -range from the data. But the model can be rather wrong on big -intervals.
+102. It is possible to get extrapolated stationary +prevalence by age ranging from agemin to agemax. + -Similarly, it is possible to get extrapolated stationary -prevalence by age raning from agemin to agemax.
+Setting bage=50 (begin age) and fage=100 (final age), makes the program computing +life expectancy from age 'bage' to age 'fage'. As we use a model, we +can interessingly compute life expectancy on a wider age range than the age +range from the data. But the model can be rather wrong on much larger +intervals. Program is limited to around 120 for upper age!
- agemin= Minimum age for calculation of the @@ -502,16 +501,120 @@ prevalence by age raning from agemin to stationary prevalence
- bage= Minimum age for calculation of the health expectancies -
- fage= Maximum ages for calculation of the health +
- fage= Maximum age for calculation of the health expectancies
Computing the observed prevalence
+ +begin-prev-date=1/1/1984 end-prev-date=1/6/1988+ +
Statements 'begin-prev-date' and 'end-prev-date' allow to +select the period in which we calculate the observed prevalences +in each state. In this example, the prevalences are calculated on +data survey collected between 1 january 1984 and 1 june 1988.
+ +-
+
- begin-prev-date= Starting date + (day/month/year) +
- end-prev-date= Final date + (day/month/year) +
Population- or status-based health +expectancies
+ +pop_based=0+ +
The program computes status-based health expectancies, i.e health
+expectancies which depends on your initial health state. If you are
+healthy your healthy life expectancy (e11) is higher than if you were
+disabled (e21, with e11 > e21).
+To compute a healthy life expectancy independant of the initial status
+we have to weight e11 and e21 according to the probability to be in
+each state at initial age or, with other word, according to the
+proportion of people in each state.
+
+We prefer computing a 'pure' period healthy life expectancy based only
+on the transtion forces. Then the weights are simply the stationnary
+prevalences or 'implied' prevalences at the initial age.
+
+Some other people would like to use the cross-sectional prevalences
+(the "Sullivan prevalences") observed at the initial age during a
+period of time defined just above.
+
+
-
+
- popbased= 0 Health expectancies are computed + at each age from stationary prevalences 'expected' at this initial age. +
- popbased= 1 Health expectancies are computed + at each age from cross-sectional 'observed' prevalence at this + initial age. As all the population is not observed at the same exact date we + define a short period were the observed prevalence is computed. +
Prevalence forecasting ( Experimental)
+ +starting-proj-date=1/1/1989 final-proj-date=1/1/1992 mov_average=0+ +
Prevalence and population projections are only available if the +interpolation unit is a month, i.e. stepm=1 and if there are no +covariate. The programme estimates the prevalence in each state at a +precise date expressed in day/month/year. The programme computes one +forecasted prevalence a year from a starting date (1 january of 1989 +in this example) to a final date (1 january 1992). The statement +mov_average allows to compute smoothed forecasted prevalences with a +five-age moving average centered at the mid-age of the five-age +period.
+ +-
+
- starting-proj-date= starting date + (day/month/year) of forecasting +
- final-proj-date= final date + (day/month/year) of forecasting +
- mov_average= smoothing with a five-age + moving average centered at the mid-age of the five-age + period. The command mov_average takes + value 1 if the prevalences are smoothed and 0 otherwise. +
Last uncommented line : Population +forecasting
+ +popforecast=0 popfile=pyram.txt popfiledate=1/1/1989 last-popfiledate=1/1/1992+ +
This command is available if the interpolation unit is a +month, i.e. stepm=1 and if popforecast=1. From a data file +including age and number of persons alive at the precise date +‘popfiledate’, you can forecast the number of persons +in each state until date ‘last-popfiledate’. In this +example, the popfile pyram.txt +includes real data which are the Japanese population in 1989.
+ +-
+
- popforecast= + 0 Option for population forecasting. If + popforecast=1, the programme does the forecasting. +
- popfile= + name of the population file +
- popfiledate= + date of the population population +
- last-popfiledate= + date of the last population projection +
Running Imach with this example
-We assume that you entered your 1st_example
+ We assume that you entered your 1st_example
parameter file as explained above. To
run the program you should click on the imach.exe icon and enter
the name of the parameter file which is for example
Once the optimization is finished, some graphics can be made
with a grapher. We use Gnuplot which is an interactive plotting
program copyrighted but freely distributed. A gnuplot reference
-manual is available here. For example, life expectancy of a healthy individual at age 70
-is 10.92 in the healthy state and 3.04 in the disability state
-(=13.96 years). If he was disable at age 70, his life expectancy
-will be shorter, 5.64 in the healthy state and 6.21 in the
-disability state (=11.85 years). The total life expectancy is a
-weighted mean of both, 13.96 and 11.85; weight is the proportion
+is 10.42 in the healthy state and 3.04 in the disability state
+(=13.46 years). If he was disable at age 70, his life expectancy
+will be shorter, 5.64 in the healthy state and 5.71 in the
+disability state (=11.35 years). The total life expectancy is a
+weighted mean of both, 13.46 and 11.35; weight is the proportion
of people disabled at age 70. In order to get a pure period index
(i.e. based only on incidences) we use the computed or
@@ -743,14 +846,14 @@ href="trbiaspar.txt">-Total life expectancy by
@@ -848,9 +951,56 @@ file: - Prevalence forecasting:
+frbiaspar.txt
+
+ First,
+we have estimated the observed prevalence between 1/1/1984 and
+1/6/1988. The mean date of interview (weighed average of the
+interviews performed between1/1/1984 and 1/6/1988) is estimated
+to be 13/9/1985, as written on the top on the file. Then we
+forecast the probability to be in each state. Example,
+at date 1/1/1989 : Since
+the minimum age is 70 on the 13/9/1985, the youngest forecasted
+age is 73. This means that at age a person aged 70 at 13/9/1989
+has a probability to enter state1 of 0.807 at age 73 on 1/1/1989.
+Similarly, the probability to be in state 2 is 0.078 and the
+probability to die is 0.115. Then, on the 1/1/1989, the
+prevalence of disability at age 73 is estimated to be 0.088. From the population file, we estimate the number of people in
+each state. At age 73, 645857 persons are in state 1 and 69320
+are in state 2. One year latter, 512892 are still in state 1,
+85003 are in state 2 and 117282 died before 1/1/1990.
+manual is available here.
When the running is finished, the user should enter a caracter
for plotting and output editing. For example 70 10.9226 3.0401 5.6488 6.2122 means:
-e11=10.9226 e12=3.0401 e21=5.6488 e22=6.2122
+For example 70 10.4227 3.0402 5.6488 5.7123 means:
+e11=10.4227 e12=3.0402 e21=5.6488 e22=5.7123
# StartingAge FinalAge P.1 P.2 P.3
+# Forecasting at date 1/1/1989
+ 73 0.807 0.078 0.115
+
+- Population forecasting:
+poprbiaspar.txt
+
+# Age P.1 P.2 P.3 [Population]
+# Forecasting at date 1/1/1989
+75 572685.22 83798.08
+74 621296.51 79767.99
+73 645857.70 69320.60
+
+# Forecasting at date 1/1/19909
+76 442986.68 92721.14 120775.48
+75 487781.02 91367.97 121915.51
+74 512892.07 85003.47 117282.76
+
+
-Trying an example
+ Trying an example
Since you know how to run the program, it is time to test it on your own computer. Try for example on a parameter file named Enter the parameter fi
| IMACH, Version 0.64b Enter
+ Enter
the parameter file name: ..\mytry\imachpar.txt |