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version 1.3, 2001/05/09 14:09:37
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version 1.9, 2002/03/11 15:24:05
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| <title>Computing Health Expectancies using IMaCh</title>
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<title>Computing Health Expectancies using IMaCh</title>
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| <!-- Changed by: Agnes Lievre, 12-Oct-2000 -->
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<!-- Changed by: Agnes Lievre, 12-Oct-2000 -->
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Line 29 color="#00006A">INED</font></a><font col
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Line 37 color="#00006A">INED</font></a><font col
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| href="http://euroreves.ined.fr"><font color="#00006A">EUROREVES</font></a></h3>
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href="http://euroreves.ined.fr"><font color="#00006A">EUROREVES</font></a></h3>
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| <p align="center"><font color="#00006A" size="4"><strong>Version
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<p align="center"><font color="#00006A" size="4"><strong>Version
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| 64b, May 2001</strong></font></p>
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0.71a, March 2002</strong></font></p>
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| <hr size="3" color="#EC5E5E">
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<hr size="3" color="#EC5E5E">
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Line 58 color="#00006A">) </font></h4>
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Line 66 color="#00006A">) </font></h4>
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| <ul>
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<ul>
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| <li><a href="#intro">Introduction</a> </li>
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<li><a href="#intro">Introduction</a> </li>
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| <li>The detailed statistical model (<a href="docmath.pdf">PDF
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| version</a>),(<a href="docmath.ps">ps version</a>) </li>
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| <li><a href="#data">On what kind of data can it be used?</a></li>
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<li><a href="#data">On what kind of data can it be used?</a></li>
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| <li><a href="#datafile">The data file</a> </li>
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<li><a href="#datafile">The data file</a> </li>
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| <li><a href="#biaspar">The parameter file</a> </li>
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<li><a href="#biaspar">The parameter file</a> </li>
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Line 80 monitor. In low mortality countries, the
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Line 86 monitor. In low mortality countries, the
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| mortality declines, the increase in DFLE is not proportionate to
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mortality declines, the increase in DFLE is not proportionate to
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| the increase in total Life expectancy. This case is called the <em>Expansion
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the increase in total Life expectancy. This case is called the <em>Expansion
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| of morbidity</em>. Most of the data collected today, in
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of morbidity</em>. Most of the data collected today, in
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| particular by the international <a href="http://euroreves/reves">REVES</a>
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particular by the international <a href="http://www.reves.org">REVES</a>
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| network on Health expectancy, and most HE indices based on these
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network on Health expectancy, and most HE indices based on these
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| data, are <em>cross-sectional</em>. It means that the information
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data, are <em>cross-sectional</em>. It means that the information
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| collected comes from a single cross-sectional survey: people from
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collected comes from a single cross-sectional survey: people from
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Line 181 according to parameters: selection of a
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Line 187 according to parameters: selection of a
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| absorbing and non-absorbing states, number of waves taken in
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absorbing and non-absorbing states, number of waves taken in
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| account (the user inputs the first and the last interview), a
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account (the user inputs the first and the last interview), a
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| tolerance level for the maximization function, the periodicity of
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tolerance level for the maximization function, the periodicity of
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| the transitions (we can compute annual, quaterly or monthly
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the transitions (we can compute annual, quarterly or monthly
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| transitions), covariates in the model. It works on Windows or on
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transitions), covariates in the model. It works on Windows or on
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| Unix.<br>
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Unix.<br>
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| </p>
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</p>
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Line 273 weights or covariates, you must fill the
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Line 279 weights or covariates, you must fill the
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| <h2><font color="#00006A">Your first example parameter file</font><a
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<h2><font color="#00006A">Your first example parameter file</font><a
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| href="http://euroreves.ined.fr/imach"></a><a name="uio"></a></h2>
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href="http://euroreves.ined.fr/imach"></a><a name="uio"></a></h2>
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| <h2><a name="biaspar"></a>#Imach version 0.64b, May 2001,
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<h2><a name="biaspar"></a>#Imach version 0.71a, March 2002,
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| INED-EUROREVES </h2>
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INED-EUROREVES </h2>
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| <p>This is a comment. Comments start with a '#'.</p>
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<p>This is a comment. Comments start with a '#'.</p>
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Line 321 line</font></a></h4>
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Line 327 line</font></a></h4>
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| <li>... </li>
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<li>... </li>
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| </ul>
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</ul>
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| </li>
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</li>
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| <li><b>ncov=2</b> Number of covariates in the datafile. The
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<li><b>ncov=2</b> Number of covariates in the datafile. </li>
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| intercept and the age parameter are counting for 2
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| covariates.</li>
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| <li><b>nlstate=2</b> Number of non-absorbing (alive) states.
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<li><b>nlstate=2</b> Number of non-absorbing (alive) states.
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| Here we have two alive states: disability-free is coded 1
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Here we have two alive states: disability-free is coded 1
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| and disability is coded 2. </li>
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and disability is coded 2. </li>
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Line 349 line</font></a></h4>
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Line 353 line</font></a></h4>
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| <h4><font color="#FF0000">Covariates</font></h4>
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<h4><font color="#FF0000">Covariates</font></h4>
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| <p>Intercept and age are systematically included in the model.
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<p>Intercept and age are systematically included in the model.
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| Additional covariates can be included with the command </p>
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Additional covariates can be included with the command: </p>
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| <pre>model=<em>list of covariates</em></pre>
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<pre>model=<em>list of covariates</em></pre>
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Line 369 Additional covariates can be included wi
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Line 373 Additional covariates can be included wi
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| the product covariate*age</li>
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the product covariate*age</li>
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| </ul>
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</ul>
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<p>In this example, we have two covariates in the data file
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(fields 2 and 3). The number of covariates is defined with
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statement ncov=2. If now you have 3 covariates in the datafile
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(fields 2, 3 and 4), you have to set ncov=3. Then you can run the
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programme with a new parametrisation taking into account the
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third covariate. For example, <strong>model=V1+V3 </strong>estimates
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a model with the first and third covariates. More complicated
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models can be used, but it will takes more time to converge. With
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a simple model (no covariates), the programme estimates 8
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parameters. Adding covariates increases the number of parameters
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: 12 for <strong>model=V1, </strong>16 for <strong>model=V1+V1*age
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</strong>and 20 for <strong>model=V1+V2+V3.</strong></p>
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| <h4><font color="#FF0000">Guess values for optimization</font><font
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<h4><font color="#FF0000">Guess values for optimization</font><font
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| color="#00006A"> </font></h4>
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color="#00006A"> </font></h4>
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Line 387 initials values, a12, b12, a13, b13, a21
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Line 404 initials values, a12, b12, a13, b13, a21
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| start with zeros as in this example, but if you have a more
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start with zeros as in this example, but if you have a more
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| precise set (for example from an earlier run) you can enter it
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precise set (for example from an earlier run) you can enter it
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| and it will speed up them<br>
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and it will speed up them<br>
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| Each of the four lines starts with indices "ij": <br>
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Each of the four lines starts with indices "ij": <b>ij
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| <br>
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aij bij</b> </p>
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| <b>ij aij bij</b> </p>
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| <blockquote>
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<blockquote>
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| <pre># Guess values of aij and bij in log (pij/pii) = aij + bij * age
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<pre># Guess values of aij and bij in log (pij/pii) = aij + bij * age
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Line 399 Each of the four lines starts with indic
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Line 415 Each of the four lines starts with indic
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| 23 -6.234642 0.022315 </pre>
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23 -6.234642 0.022315 </pre>
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| </blockquote>
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</blockquote>
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| <p>or, to simplify: </p>
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<p>or, to simplify (in most of cases it converges but there is no
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warranty!): </p>
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| <blockquote>
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<blockquote>
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| <pre>12 0.0 0.0
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<pre>12 0.0 0.0
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Line 408 Each of the four lines starts with indic
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Line 425 Each of the four lines starts with indic
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| 23 0.0 0.0</pre>
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23 0.0 0.0</pre>
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| </blockquote>
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</blockquote>
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<p> In order to speed up the convergence you can make a first run with
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a large stepm i.e stepm=12 or 24 and then decrease the stepm until
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stepm=1 month. If newstepm is the new shorter stepm and stepm can be
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expressed as a multiple of newstepm, like newstepm=n stepm, then the
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following approximation holds:
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<pre>aij(n stepm) = aij(stepm) +ln(n)
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</pre> and
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<pre>bij(n stepm) = bij(stepm) .</pre>
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| <h4><font color="#FF0000">Guess values for computing variances</font></h4>
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<h4><font color="#FF0000">Guess values for computing variances</font></h4>
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| <p>This is an output if <a href="#mle">mle</a>=1. But it can be
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<p>This is an output if <a href="#mle">mle</a>=1. But it can be
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| used as an input to get the vairous output data files (Health
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used as an input to get the various output data files (Health
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| expectancies, stationary prevalence etc.) and figures without
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expectancies, stationary prevalence etc.) and figures without
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| rerunning the rather long maximisation phase (mle=0). </p>
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rerunning the rather long maximisation phase (mle=0). </p>
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Line 442 consists in indices "ij" follo
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Line 467 consists in indices "ij" follo
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| <h4><font color="#FF0000">Covariance matrix of parameters</font></h4>
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<h4><font color="#FF0000">Covariance matrix of parameters</font></h4>
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| <p>This is an output if <a href="#mle">mle</a>=1. But it can be
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<p>This is an output if <a href="#mle">mle</a>=1. But it can be
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| used as an input to get the vairous output data files (Health
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used as an input to get the various output data files (Health
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| expectancies, stationary prevalence etc.) and figures without
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expectancies, stationary prevalence etc.) and figures without
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| rerunning the rather long maximisation phase (mle=0). </p>
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rerunning the rather long maximisation phase (mle=0). </p>
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Line 477 covariances between aij and bij: </p>
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Line 502 covariances between aij and bij: </p>
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| </li>
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</li>
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| </ul>
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</ul>
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| <h4><a name="biaspar-l"></a><font color="#FF0000">last
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<h4><font color="#FF0000">Age range for calculation of stationary
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| uncommented line</font></h4>
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prevalences and health expectancies</font></h4>
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| <pre>agemin=70 agemax=100 bage=50 fage=100</pre>
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<pre>agemin=70 agemax=100 bage=50 fage=100</pre>
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| <p>Once we obtained the estimated parameters, the program is able
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<p>Once we obtained the estimated parameters, the program is able
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| to calculated stationary prevalence, transitions probabilities
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to calculated stationary prevalence, transitions probabilities
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| and life expectancies at any age. Choice of age ranges is useful
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and life expectancies at any age. Choice of age range is useful
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| for extrapolation. In our data file, ages varies from age 70 to
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for extrapolation. In our data file, ages varies from age 70 to
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| 102. Setting bage=50 and fage=100, makes the program computing
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102. It is possible to get extrapolated stationary prevalence by
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| life expectancy from age bage to age fage. As we use a model, we
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age ranging from agemin to agemax. </p>
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| can compute life expectancy on a wider age range than the age
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| range from the data. But the model can be rather wrong on big
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| intervals.</p>
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| <p>Similarly, it is possible to get extrapolated stationary
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<p>Setting bage=50 (begin age) and fage=100 (final age), makes
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| prevalence by age raning from agemin to agemax. </p>
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the program computing life expectancy from age 'bage' to age
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'fage'. As we use a model, we can interessingly compute life
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expectancy on a wider age range than the age range from the data.
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But the model can be rather wrong on much larger intervals.
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Program is limited to around 120 for upper age!</p>
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| <ul>
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<ul>
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| <li><b>agemin=</b> Minimum age for calculation of the
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<li><b>agemin=</b> Minimum age for calculation of the
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Line 502 prevalence by age raning from agemin to
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Line 528 prevalence by age raning from agemin to
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| stationary prevalence </li>
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stationary prevalence </li>
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| <li><b>bage=</b> Minimum age for calculation of the health
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<li><b>bage=</b> Minimum age for calculation of the health
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| expectancies </li>
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expectancies </li>
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| <li><b>fage=</b> Maximum ages for calculation of the health
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<li><b>fage=</b> Maximum age for calculation of the health
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| expectancies </li>
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expectancies </li>
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| </ul>
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</ul>
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<h4><a name="Computing"><font color="#FF0000">Computing</font></a><font
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color="#FF0000"> the observed prevalence</font></h4>
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<pre>begin-prev-date=1/1/1984 end-prev-date=1/6/1988 </pre>
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<p>Statements 'begin-prev-date' and 'end-prev-date' allow to
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select the period in which we calculate the observed prevalences
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in each state. In this example, the prevalences are calculated on
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data survey collected between 1 january 1984 and 1 june 1988. </p>
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<ul>
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<li><strong>begin-prev-date= </strong>Starting date
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(day/month/year)</li>
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<li><strong>end-prev-date= </strong>Final date
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(day/month/year)</li>
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</ul>
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<h4><font color="#FF0000">Population- or status-based health
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expectancies</font></h4>
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<pre>pop_based=0</pre>
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<p>The program computes status-based health expectancies, i.e
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health expectancies which depends on your initial health state.
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If you are healthy your healthy life expectancy (e11) is higher
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than if you were disabled (e21, with e11 > e21).<br>
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To compute a healthy life expectancy independant of the initial
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status we have to weight e11 and e21 according to the probability
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to be in each state at initial age or, with other word, according
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to the proportion of people in each state.<br>
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We prefer computing a 'pure' period healthy life expectancy based
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only on the transtion forces. Then the weights are simply the
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stationnary prevalences or 'implied' prevalences at the initial
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age.<br>
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Some other people would like to use the cross-sectional
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prevalences (the "Sullivan prevalences") observed at
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the initial age during a period of time <a href="#Computing">defined
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just above</a>. </p>
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<ul>
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<li><strong>popbased= 0 </strong>Health expectancies are
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computed at each age from stationary prevalences
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'expected' at this initial age.</li>
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<li><strong>popbased= 1 </strong>Health expectancies are
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computed at each age from cross-sectional 'observed'
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prevalence at this initial age. As all the population is
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not observed at the same exact date we define a short
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period were the observed prevalence is computed.</li>
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</ul>
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<h4><font color="#FF0000">Prevalence forecasting ( Experimental)</font></h4>
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<pre>starting-proj-date=1/1/1989 final-proj-date=1/1/1992 mov_average=0 </pre>
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<p>Prevalence and population projections are only available if
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the interpolation unit is a month, i.e. stepm=1 and if there are
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no covariate. The programme estimates the prevalence in each
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state at a precise date expressed in day/month/year. The
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programme computes one forecasted prevalence a year from a
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starting date (1 january of 1989 in this example) to a final date
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(1 january 1992). The statement mov_average allows to compute
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smoothed forecasted prevalences with a five-age moving average
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centered at the mid-age of the five-age period. </p>
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<ul>
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<li><strong>starting-proj-date</strong>= starting date
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(day/month/year) of forecasting</li>
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<li><strong>final-proj-date= </strong>final date
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(day/month/year) of forecasting</li>
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<li><strong>mov_average</strong>= smoothing with a five-age
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moving average centered at the mid-age of the five-age
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period. The command<strong> mov_average</strong> takes
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value 1 if the prevalences are smoothed and 0 otherwise.</li>
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</ul>
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<h4><font color="#FF0000">Last uncommented line : Population
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forecasting </font></h4>
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<pre>popforecast=0 popfile=pyram.txt popfiledate=1/1/1989 last-popfiledate=1/1/1992</pre>
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<p>This command is available if the interpolation unit is a
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month, i.e. stepm=1 and if popforecast=1. From a data file
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including age and number of persons alive at the precise date
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‘popfiledate’, you can forecast the number of persons
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in each state until date ‘last-popfiledate’. In this
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example, the popfile <a href="pyram.txt"><b>pyram.txt</b></a>
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includes real data which are the Japanese population in 1989.</p>
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<ul type="disc">
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<li class="MsoNormal"
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style="TEXT-ALIGN: justify; mso-margin-top-alt: auto; mso-margin-bottom-alt: auto; mso-list: l10 level1 lfo36; tab-stops: list 36.0pt"><b>popforecast=
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0 </b>Option for population forecasting. If
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popforecast=1, the programme does the forecasting<b>.</b></li>
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<li class="MsoNormal"
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style="TEXT-ALIGN: justify; mso-margin-top-alt: auto; mso-margin-bottom-alt: auto; mso-list: l10 level1 lfo36; tab-stops: list 36.0pt"><b>popfile=
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</b>name of the population file</li>
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<li class="MsoNormal"
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style="TEXT-ALIGN: justify; mso-margin-top-alt: auto; mso-margin-bottom-alt: auto; mso-list: l10 level1 lfo36; tab-stops: list 36.0pt"><b>popfiledate=</b>
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date of the population population</li>
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<li class="MsoNormal"
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style="TEXT-ALIGN: justify; mso-margin-top-alt: auto; mso-margin-bottom-alt: auto; mso-list: l10 level1 lfo36; tab-stops: list 36.0pt"><b>last-popfiledate</b>=
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date of the last population projection </li>
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</ul>
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| <hr>
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<hr>
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| <h2><a name="running"></a><font color="#00006A">Running Imach
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<h2><a name="running"></a><font color="#00006A">Running Imach
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| with this example</font></h2>
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with this example</font></h2>
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|
|
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| <p>We assume that you entered your <a href="biaspar.txt">1st_example
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<p>We assume that you entered your <a href="biaspar.imach">1st_example
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| parameter file</a> as explained <a href="#biaspar">above</a>. To
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parameter file</a> as explained <a href="#biaspar">above</a>. To
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| run the program you should click on the imach.exe icon and enter
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run the program you should click on the imach.exe icon and enter
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| the name of the parameter file which is for example <a
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the name of the parameter file which is for example <a
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Line 536 and graphs</font> </a></h2>
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Line 666 and graphs</font> </a></h2>
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| <p>Once the optimization is finished, some graphics can be made
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<p>Once the optimization is finished, some graphics can be made
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| with a grapher. We use Gnuplot which is an interactive plotting
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with a grapher. We use Gnuplot which is an interactive plotting
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| program copyrighted but freely distributed. A gnuplot reference
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program copyrighted but freely distributed. A gnuplot reference
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| manual is available <a href="http://www.gnuplot.org/">here</a>. <br>
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manual is available <a href="http://www.gnuplot.info/">here</a>. <br>
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| When the running is finished, the user should enter a caracter
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When the running is finished, the user should enter a caracter
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| for plotting and output editing. </p>
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for plotting and output editing. </p>
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|
Line 705 href="erbiaspar.txt"><b>erbiaspar.txt</b
|
Line 835 href="erbiaspar.txt"><b>erbiaspar.txt</b
|
| 72 9.9667 3.0502 4.8663 6.1025
|
72 9.9667 3.0502 4.8663 6.1025
|
| 73 9.5077 3.0524 4.5044 6.0401 </pre>
|
73 9.5077 3.0524 4.5044 6.0401 </pre>
|
|
|
|
| <pre>For example 70 10.9226 3.0401 5.6488 6.2122 means:
|
<pre>For example 70 10.4227 3.0402 5.6488 5.7123 means:
|
| e11=10.9226 e12=3.0401 e21=5.6488 e22=6.2122</pre>
|
e11=10.4227 e12=3.0402 e21=5.6488 e22=5.7123</pre>
|
|
|
|
| <pre><img src="expbiaspar21.gif" width="400" height="300"><img
|
<pre><img src="expbiaspar21.gif" width="400" height="300"><img
|
| src="expbiaspar11.gif" width="400" height="300"></pre>
|
src="expbiaspar11.gif" width="400" height="300"></pre>
|
|
|
|
| <p>For example, life expectancy of a healthy individual at age 70
|
<p>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
|
is 10.42 in the healthy state and 3.04 in the disability state
|
| (=13.96 years). If he was disable at age 70, his life expectancy
|
(=13.46 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
|
will be shorter, 5.64 in the healthy state and 5.71 in the
|
| disability state (=11.85 years). The total life expectancy is a
|
disability state (=11.35 years). The total life expectancy is a
|
| weighted mean of both, 13.96 and 11.85; weight is the proportion
|
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
|
of people disabled at age 70. In order to get a pure period index
|
| (i.e. based only on incidences) we use the <a
|
(i.e. based only on incidences) we use the <a
|
| href="#Stationary prevalence in each state">computed or
|
href="#Stationary prevalence in each state">computed or
|
|
Line 743 href="trbiaspar.txt"><font face="Courier
|
Line 873 href="trbiaspar.txt"><font face="Courier
|
|
|
|
| <pre>#Total LEs with variances: e.. (std) e.1 (std) e.2 (std) </pre>
|
<pre>#Total LEs with variances: e.. (std) e.1 (std) e.2 (std) </pre>
|
|
|
|
| <pre>70 13.76 (0.22) 10.40 (0.20) 3.35 (0.14) </pre>
|
<pre>70 13.26 (0.22) 9.95 (0.20) 3.30 (0.14) </pre>
|
|
|
|
| <p>Thus, at age 70 the total life expectancy, e..=13.76years is
|
<p>Thus, at age 70 the total life expectancy, e..=13.26 years is
|
| the weighted mean of e1.=13.96 and e2.=11.85 by the stationary
|
the weighted mean of e1.=13.46 and e2.=11.35 by the stationary
|
| prevalence at age 70 which are 0.90134 in state 1 and 0.09866 in
|
prevalence at age 70 which are 0.90134 in state 1 and 0.09866 in
|
| state 2, respectively (the sum is equal to one). e.1=10.40 is the
|
state 2, respectively (the sum is equal to one). e.1=9.95 is the
|
| Disability-free life expectancy at age 70 (it is again a weighted
|
Disability-free life expectancy at age 70 (it is again a weighted
|
| mean of e11 and e21). e.2=3.35 is also the life expectancy at age
|
mean of e11 and e21). e.2=3.30 is also the life expectancy at age
|
| 70 to be spent in the disability state.</p>
|
70 to be spent in the disability state.</p>
|
|
|
|
| <h5><font color="#EC5E5E" size="3"><b>-Total life expectancy by
|
<h5><font color="#EC5E5E" size="3"><b>-Total life expectancy by
|
|
Line 848 file</b></font><b>: </b><a href="orbiasp
|
Line 978 file</b></font><b>: </b><a href="orbiasp
|
| <p>This copy of the parameter file can be useful to re-run the
|
<p>This copy of the parameter file can be useful to re-run the
|
| program while saving the old output files. </p>
|
program while saving the old output files. </p>
|
|
|
|
| |
<h5><font color="#EC5E5E" size="3"><b>- Prevalence forecasting</b></font><b>:
|
| |
</b><a href="frbiaspar.txt"><b>frbiaspar.txt</b></a></h5>
|
| |
|
| |
<p
|
| |
style="TEXT-ALIGN: justify; tab-stops: 45.8pt 91.6pt 137.4pt 183.2pt 229.0pt 274.8pt 320.6pt 366.4pt 412.2pt 458.0pt 503.8pt 549.6pt 595.4pt 641.2pt 687.0pt 732.8pt">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. </p>
|
| |
|
| |
<p
|
| |
style="TEXT-ALIGN: justify; tab-stops: 45.8pt 91.6pt 137.4pt 183.2pt 229.0pt 274.8pt 320.6pt 366.4pt 412.2pt 458.0pt 503.8pt 549.6pt 595.4pt 641.2pt 687.0pt 732.8pt">Example,
|
| |
at date 1/1/1989 : </p>
|
| |
|
| |
<pre class="MsoNormal"># StartingAge FinalAge P.1 P.2 P.3
|
| |
# Forecasting at date 1/1/1989
|
| |
73 0.807 0.078 0.115</pre>
|
| |
|
| |
<p
|
| |
style="TEXT-ALIGN: justify; tab-stops: 45.8pt 91.6pt 137.4pt 183.2pt 229.0pt 274.8pt 320.6pt 366.4pt 412.2pt 458.0pt 503.8pt 549.6pt 595.4pt 641.2pt 687.0pt 732.8pt">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.</p>
|
| |
|
| |
<h5><font color="#EC5E5E" size="3"><b>- Population forecasting</b></font><b>:
|
| |
</b><a href="poprbiaspar.txt"><b>poprbiaspar.txt</b></a></h5>
|
| |
|
| |
<pre># 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 </pre>
|
| |
|
| |
<pre># 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 </pre>
|
| |
|
| |
<p>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.</p>
|
| |
|
| <hr>
|
<hr>
|
|
|
|
| <h2><a name="example" </a><font color="#00006A">Trying an example</font></a></h2>
|
<h2><a name="example"></a><font color="#00006A">Trying an example</font></h2>
|
|
|
|
| <p>Since you know how to run the program, it is time to test it
|
<p>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 <a
|
on your own computer. Try for example on a parameter file named <a
|
|
Line 867 question:'<strong>Enter the parameter fi
|
Line 1044 question:'<strong>Enter the parameter fi
|
|
|
|
| <table border="1">
|
<table border="1">
|
| <tr>
|
<tr>
|
| <td width="100%"><strong>IMACH, Version 0.64b</strong><p><strong>Enter
|
<td width="100%"><strong>IMACH, Version 0.71</strong><p><strong>Enter
|
| the parameter file name: ..\mytry\imachpar.txt</strong></p>
|
the parameter file name: ..\mytry\imachpar.txt</strong></p>
|
| </td>
|
</td>
|
| </tr>
|
</tr>
|
|
Line 1006 edit the master file mypar.htm. </font><
|
Line 1183 edit the master file mypar.htm. </font><
|
| - Health expectancies with their variances: <a
|
- Health expectancies with their variances: <a
|
| href="..\mytry\trmypar.txt">trmypar.txt</a> <br>
|
href="..\mytry\trmypar.txt">trmypar.txt</a> <br>
|
| - Standard deviation of stationary prevalence: <a
|
- Standard deviation of stationary prevalence: <a
|
| href="..\mytry\vplrmypar.txt">vplrmypar.txt</a> <br>
|
href="..\mytry\vplrmypar.txt">vplrmypar.txt</a><br>
|
| |
- Prevalences forecasting: <a href="frmypar.txt">frmypar.txt</a>
|
| <br>
|
<br>
|
| |
- Population forecasting (if popforecast=1): <a
|
| |
href="poprmypar.txt">poprmypar.txt</a> <br>
|
| </li>
|
</li>
|
| <li><u>Graphs</u> <br>
|
<li><u>Graphs</u> <br>
|
| <br>
|
<br>
|
|
Line 1037 simple justification (name, email, insti
|
Line 1217 simple justification (name, email, insti
|
| href="mailto:brouard@ined.fr">mailto:brouard@ined.fr</a> and <a
|
href="mailto:brouard@ined.fr">mailto:brouard@ined.fr</a> and <a
|
| href="mailto:lievre@ined.fr">mailto:lievre@ined.fr</a> .</p>
|
href="mailto:lievre@ined.fr">mailto:lievre@ined.fr</a> .</p>
|
|
|
|
| <p>Latest version (0.64b of may 2001) can be accessed at <a
|
<p>Latest version (0.71a of March 2002) can be accessed at <a
|
| href="http://euroeves.ined.fr/imach">http://euroreves.ined.fr/imach</a><br>
|
href="http://euroreves.ined.fr/imach">http://euroreves.ined.fr/imach</a><br>
|
| </p>
|
</p>
|
| </body>
|
</body>
|
| </html>
|
</html>
|
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