Annexes
Modèle 1
. sort dgest
. by dgest: ologit visite reg1 reg2 reg3 reg4 reg5 reg6 reg7 reg8
reg9 reg10 re
> g11 res1 res2
Premier trimestre
-> dgest=1er trime
Ordered logit estimates Number of obs
= 904
LR chi2(12)
= 77.21
Prob > chi2
= 0.0000
Log likelihood = -864.12454 Pseudo R2
= 0.0428
------------------------------------------------------------------------------
visite | Coef. Std. Err. z P>|z| [95%
Conf. Interval]
---------+--------------------------------------------------------------------
reg1 | -.6506023 .6488384 -1.003 0.316
-1.922302 .6210977
reg2 | .1138362 .5581112 0.204 0.838
-.9800416 1.207714
reg3 | -.8386637 .6358319 -1.319 0.187
-2.084871 .4075438
reg4 | -.6936018 .6212148 -1.117 0.264
-1.911161 .5239568
reg5 | -1.039533 .5467882 -1.901 0.057
-2.111218 .0321524
reg6 | -.8477687 .5687559 -1.491 0.136
-1.96251 .2669723
reg7 | -1.040383 .5251298 -1.981 0.048
-2.069618 -.0111472
reg8 | -.5313331 .5240567 -1.014 0.311
-1.558465 .4957992
reg9 | -.2757231 .6280454 -0.439 0.661
-1.506669 .9552232
reg11 | -.0981098 .5560191 -0.176 0.860
-1.187887 .9916675
res1 | .9514504 .5135148 1.132 0.000
-.4250202 1.587921
res2 | .5859161 .1765702 5.414 0.258
.6098448 1.301987
---------+--------------------------------------------------------------------
_cut1 | -3.128396 .5218087 (Ancillary
parameters)
_cut2 | -1.800142 .5043852
_cut3 | -.3299721 .4997581
------------------------------------------------------------------------------
Au-delà du premier trimestre
-> dgest=au delà d
Ordered logit estimates Number of obs
= 1066
LR chi2(12)
= 92.93
Prob > chi2
= 0.0000
Log likelihood = -1383.2308 Pseudo R2
= 0.0325
------------------------------------------------------------------------------
visite | Coef. Std. Err. z P>|z| [95%
Conf. Interval]
---------+--------------------------------------------------------------------
reg1 | -.6340418 .56168 -1.129 0.259
-1.734914 .4668307
reg2 | .056682 .5208942 0.109 0.913
-.9642519 1.077616
reg3 | -.0082717 .5706933 -0.014 0.988
-1.12681 1.110267
reg4 | .5440493 .5465382 0.995 0.320
-.5271458 1.615244
reg5 | -.0297202 .4939171 -0.060 0.952
-.9977799 .9383395
reg6 | 1.111163 .5120629 2.170 0.030
.1075386 2.114788
reg7 | .4134281 .5013769 0.825 0.410
-.5692526 1.396109
reg8 | .4362273 .4868521 0.896 0.370
-.5179854 1.39044
reg9 | .0293975 .5982332 0.049 0.961
-1.143118 1.201913
reg11 | .0993553 .5229265 0.190 0.849
-.9255618 1.124272
res1 | 1.485456 .4994595 2.974 0.003
.5065329 2.464378
res2 | .6465083 .1345434 4.805 0.000
.3828081 .9102084
---------+--------------------------------------------------------------------
_cut1 | -1.224531 .4850002 (Ancillary
parameters)
_cut2 | .2769385 .4826834
_cut3 | 1.563914 .4849692
------------------------------------------------------------------------------
Modèle 2
. by dgest: ologit visite reg1 reg2 reg3 reg4 reg5 reg6 reg7 reg8
reg9 reg10 re
> g11 res1 res2 eth1 eth2 eth3 eth4 eth5 eth6 eth8 eth9 soc1
soc2 rel2 rel3
Premier trimestre
-> dgest=1er trime
Ordered logit estimates Number of obs
= 842
LR chi2(24)
= 94.33
Prob > chi2
= 0.0000
Log likelihood = -798.82834 Pseudo R2
= 0.0557
------------------------------------------------------------------------------
visite | Coef. Std. Err. z P>|z| [95%
Conf. Interval]
---------+--------------------------------------------------------------------
reg1 | .0449053 .6695047 0.067 0.947
-1.2673 1.35711
reg2 | .5195527 .5412977 0.960 0.337
-.5413713 1.580477
reg4 | -.3782965 .6371373 -0.594 0.553
-1.627063 .8704697
reg5 | -.4976074 .5341488 -0.932 0.352
-1.54452 .5493051
reg6 | -.4567127 .5619284 -0.813 0.416
-1.558072 .6446467
reg7 | -.5232008 .5554042 -0.942 0.346
-1.611773 .5653714
reg8 | -.0381037 .5123103 -0.074 0.941
-1.042213 .9660061
reg9 | .4100814 .6220717 0.659 0.510
-.8091568 1.62932
reg10 | .7030753 .7002467 1.004 0.315
-.6693831 2.075534
reg11 | .7122655 .6046243 1.178 0.239
-.4727764 1.897307
res1 | .7543989 .4942658 1.526 0.004
-.2143442 1.723142
res2 | .6358492 .2205161 2.883 0.127
.2036456 1.068053
eth1 | .2448556 .4683966 0.523 0.601
-.6731848 1.162896
eth2 | -.2764913 .3793372 -0.729 0.466
-1.019979 .4669959
eth3 | -.6291257 .442943 -1.420 0.156
-1.497278 .2390266
eth4 | -.2975475 .3790643 -0.785 0.432
-1.0405 .4454048
eth5 | -.9265582 .4979175 -1.861 0.063
-1.902459 .0493421
eth6 | -.7509729 .4072538 -1.844 0.065
-1.549176 .0472298
eth8 | -.473689 .3999771 -1.184 0.236
-1.25763 .3102517
eth9 | .0666485 .3317664 0.201 0.841
-.5836016 .7168986
soc1 | .5477556 .2300975 2.381 0.017
.0967728 .9987383
soc2 | .4525897 .2028823 2.231 0.026
.0549476 .8502318
rel2 | .2701501 .3036617 0.890 0.374
-.325016 .8653162
rel3 | -.6160508 .3820525 -1.612 0.107
-1.36486 .1327584
---------+--------------------------------------------------------------------
_cut1 | -2.664457 .5157327 (Ancillary
parameters)
_cut2 | -1.337778 .4976202
_cut3 | .1806721 .4950026
----------------------------------------------------------------------²²--------
Au-delà du premier trimestre
-> dgest=au delà d
Ordered logit estimates Number of obs
= 1011
LR chi2(24)
= 114.84
Prob > chi2
= 0.0000
Log likelihood = -1300.3384 Pseudo R2
= 0.0423
------------------------------------------------------------------------------
visite | Coef. Std. Err. z P>|z| [95%
Conf. Interval]
---------+--------------------------------------------------------------------
reg1 | -.8064842 .5038774 -1.601 0.109
-1.794066 .1810975
reg2 | -.3122519 .4953503 -0.630 0.528
-1.283121 .6586169
reg3 | -.0982997 .5736414 -0.171 0.864
-1.222616 1.026017
reg4 | .1971688 .5396071 0.365 0.715
-.8604416 1.254779
reg5 | -.5755903 .4852819 -1.186 0.236
-1.526725 .3755447
reg6 | .5542502 .4983022 1.112 0.266
-.4224042 1.530905
reg7 | -.3758337 .5158638 -0.729 0.466
-1.386908 .6352407
reg8 | -.1511354 .4706858 -0.321 0.748
-1.073663 .7713918
reg10 | -.4273932 .6489811 -0.659 0.510
-1.699373 .8445863
reg11 | -.3723095 .5370099 -0.693 0.488
-1.42483 .6802106
res1 | 1.145093 .4800202 2.386 0.007
.2042703 2.085915
res2 | .5740457 .1820976 3.152 0.002
.2171409 .9309505
eth1 | -.119008 .4068755 -0.292 0.770
-.9164694 .6784534
eth2 | .2499687 .3693537 0.677 0.499
-.4739513 .9738887
eth3 | -.3514443 .4415874 -0.796 0.426
-1.21694 .514051
eth4 | .0351044 .3339412 0.105 0.916
-.6194084 .6896172
eth5 | -1.259924 .4676525 -2.694 0.007
-2.176506 -.3433415
eth6 | -.4934328 .4489211 -1.099 0.272
-1.373302 .3864363
eth8 | -.1034853 .3143972 -0.329 0.742
-.7196925 .5127219
eth9 | .2973032 .2889358 1.029 0.303
-.2690005 .863607
soc1 | .1156505 .2287323 0.506 0.613
-.3326566 .5639576
soc2 | .2805656 .167297 1.677 0.094
-.0473304 .6084616
rel2 | -.249249 .2911102 -0.856 0.392
-.8198145 .3213165
rel3 | .646519 .3329245 1.942 0.052
-.006001 1.299039
---------+--------------------------------------------------------------------
_cut1 | -1.758377 .4735409 (Ancillary
parameters)
_cut2 | -.2004172 .4685664
_cut3 | 1.121039 .4704237
------------------------------------------------------------------------------
Modèle 3
. by dgest: ologit visite reg1 reg2 reg3 reg4 reg5 reg6 reg7 reg8
reg9 reg10 re
> g11 res1 res2 eth1 eth2 eth3 eth4 eth5 eth6 eth8 eth9 soc1
soc2 rel2 rel3 vie
> 1 vie2
Premier trimestre
-> dgest=1er trime
Ordered logit estimates Number of obs
= 842
LR chi2(26)
= 105.20
Prob > chi2
= 0.0000
Log likelihood = -793.38946 Pseudo R2
= 0.0622
------------------------------------------------------------------------------
visite | Coef. Std. Err. z P>|z| [95%
Conf. Interval]
---------+--------------------------------------------------------------------
reg1 | .17409 .6724007 0.259 0.796
-1.143791 1.491971
reg2 | .4121701 .5401768 0.763 0.445
-.646557 1.470897
reg4 | -.3258011 .6405056 -0.509 0.611
-1.581169 .9295667
reg5 | -.5331129 .535718 -0.995 0.320
-1.583101 .5168751
reg6 | -.3739845 .5625319 -0.665 0.506
-1.476527 .7285577
reg7 | -.5071225 .5555447 -0.913 0.361
-1.59597 .5817251
reg8 | .0111218 .5131167 0.022 0.983
-.9945685 1.016812
reg9 | .3938898 .625708 0.630 0.529
-.8324754 1.620255
reg10 | .7524921 .7001282 1.075 0.282
-.6197339 2.124718
reg11 | .6953783 .6032506 1.153 0.249
-.4869712 1.877728
res1 | .8454552 .4950246 1.708 0.008
-.1247751 1.815686
res2 | .8656471 .2344359 3.692 0.189
.4061612 1.325133
eth1 | -.0091249 .4846542 -0.019 0.985
-.9590297 .9407799
eth2 | -.3413535 .3828026 -0.892 0.373
-1.091633 .4089259
eth3 | -.6852698 .4470602 -1.533 0.125
-1.561492 .190952
eth4 | -.3815228 .382879 -0.996 0.319
-1.131952 .3689062
eth5 | -.9322883 .4996614 -1.866 0.062
-1.911607 .0470301
eth6 | -.7820477 .4094497 -1.910 0.056
-1.584554 .020459
eth8 | -.4560055 .399807 -1.141 0.254
-1.239613 .3276019
eth9 | -.081805 .335622 -0.244 0.807
-.7396121 .576002
soc1 | .4616164 .2331584 1.980 0.048
.0046344 .9185984
soc2 | .3789486 .2050622 1.848 0.065
-.0229659 .7808632
rel2 | .3044187 .3067744 0.992 0.321
-.296848 .9056855
rel3 | -.4552531 .3855721 -1.181 0.238
-1.210961 .3004543
vie1 | .8728385 .350741 2.489 0.013
.1853989 1.560278
vie2 | .4377687 .1695241 2.582 0.010
.1055076 .7700299
---------+--------------------------------------------------------------------
_cut1 | -2.439632 .5206745 (Ancillary
parameters)
_cut2 | -1.106083 .5031476
_cut3 | .4277796 .501483
------------------------------------------------------------------------------
Au-delà du premier trimestre
-> dgest=au delà d
Ordered logit estimates Number of obs
= 1011
LR chi2(26)
= 126.38
Prob > chi2
= 0.0000
Log likelihood = -1294.5697 Pseudo R2
= 0.0465
------------------------------------------------------------------------------
visite | Coef. Std. Err. z P>|z| [95%
Conf. Interval]
---------+--------------------------------------------------------------------
reg1 | -.3927104 .5896868 -0.666 0.505
-1.548475 .7630544
reg2 | .0258456 .5483878 0.047 0.962
-1.048975 1.100666
reg3 | .3605326 .5980468 0.603 0.547
-.8116176 1.532683
reg4 | .7478822 .6008655 1.245 0.213
-.4297925 1.925557
reg5 | -.1326811 .5424415 -0.245 0.807
-1.195847 .9304848
reg6 | 1.015028 .5577638 1.820 0.069
-.0781693 2.108224
reg7 | .1144824 .5701868 0.201 0.841
-1.003063 1.232028
reg8 | .3168949 .5301646 0.598 0.550
-.7222086 1.355998
reg9 | .3428623 .6536907 0.525 0.600
-.938348 1.624073
reg11 | .1143045 .5949117 0.192 0.848
-1.051701 1.28031
res1 | 1.54865 .5397263 2.869 0.004
.4908056 2.606494
res2 | .6602304 .1874996 3.521 0.000
.2927379 1.027723
eth1 | -.2875548 .4119605 -0.698 0.485
-1.094983 .5198729
eth2 | .2649236 .3705501 0.715 0.475
-.4613412 .9911884
eth3 | -.3443992 .4427614 -0.778 0.437
-1.212196 .5233972
eth4 | -.0129969 .3344516 -0.039 0.969
-.6685101 .6425163
eth5 | -1.213364 .4667439 -2.600 0.009
-2.128165 -.298563
eth6 | -.4565245 .4497284 -1.015 0.310
-1.337976 .424927
eth8 | -.2197533 .3177913 -0.692 0.489
-.8426129 .4031063
eth9 | .222903 .289488 0.770 0.441
-.344483 .790289
soc1 | .0852809 .231353 0.369 0.712
-.3681627 .5387244
soc2 | .251666 .1679695 1.498 0.134
-.0775481 .5808801
rel2 | -.2512357 .2916181 -0.862 0.389
-.8227967 .3203254
rel3 | .6236371 .33341 1.870 0.061
-.0298345 1.277109
vie1 | 1.19275 .377762 3.157 0.002
.4523497 1.933149
vie2 | .1733983 .1309829 1.324 0.186
-.0833234 .43012
---------+--------------------------------------------------------------------
_cut1 | -1.233063 .5359628 (Ancillary
parameters)
_cut2 | .3313123 .5337796
_cut3 | 1.663048 .536139
------------------------------------------------------------------------------
Modèle 4
. by dgest: ologit visite reg1 reg2 reg3 reg4 reg5 reg6 reg7 reg8
reg9 reg10 re
> g11 res1 res2 eth1 eth2 eth3 eth4 eth5 eth6 eth8 eth9 soc1
soc2 rel2 rel3 vi
> e1 vie2 age1 age3 par1 par3 opo2 niv2 niv3
Premier trimestre
-> dgest=1er trime
Ordered logit estimates Number of obs
= 841
LR chi2(33)
= 116.51
Prob > chi2
= 0.0000
Log likelihood = -786.35647 Pseudo R2
= 0.0690
------------------------------------------------------------------------------
visite | Coef. Std. Err. z P>|z| [95%
Conf. Interval]
---------+--------------------------------------------------------------------
reg2 | .2745509 .576992 0.476 0.634
-.8563326 1.405434
reg3 | -.1927992 .6767151 -0.285 0.776
-1.519136 1.133538
reg4 | -.5481076 .6378516 -0.859 0.390
-1.798274 .7020586
reg5 | -.5793103 .5803142 -0.998 0.318
-1.716705 .5580847
reg6 | -.5703493 .6011142 -0.949 0.343
-1.748511 .6078128
reg7 | -.6702119 .5858583 -1.144 0.253
-1.818473 .4780492
reg8 | -.1707521 .547212 -0.312 0.755
-1.243268 .9017638
reg9 | .3095176 .664301 0.466 0.641
-.9924884 1.611524
reg10 | .5858546 .7369061 0.795 0.427
-.8584548 2.030164
reg11 | .6472261 .6372639 1.016 0.310
-.6017883 1.89624
res1 | .6384057 .5393277 1.184 0.001
-.4186571 1.695468
res2 | .7743374 .241583 3.205 0.237
.3008435 1.247831
eth1 | .1121829 .4965908 0.226 0.821
-.8611171 1.085483
eth2 | -.2142377 .3937244 -0.544 0.586
-.9859233 .5574479
eth3 | -.6801104 .4549302 -1.495 0.135
-1.571757 .2115364
eth4 | -.3523063 .3890194 -0.906 0.365
-1.11477 .4101578
eth5 | -.9099381 .5107207 -1.782 0.075
-1.910932 .091056
eth6 | -.7581748 .4143635 -1.830 0.067
-1.570312 .0539627
eth8 | -.512131 .4077425 -1.256 0.209
-1.311292 .2870295
eth9 | -.0690215 .3373168 -0.205 0.838
-.7301503 .5921074
soc1 | .4067708 .2472316 1.645 0.100
-.0777944 .8913359
soc2 | .3149491 .2112062 1.491 0.136
-.0990074 .7289056
rel2 | .3778913 .3148076 1.200 0.230
-.2391202 .9949028
rel3 | -.4670949 .3882181 -1.203 0.229
-1.227988 .2937986
vie1 | .7197562 .3633412 1.981 0.048
.0076205 1.431892
vie2 | .3948748 .173547 2.275 0.023
.054729 .7350206
age1 | -.1767174 .2086453 -0.847 0.397
-.5856546 .2322198
age3 | -.0304225 .2254821 -0.135 0.893
-.4723594 .4115144
par1 | .0683205 .207793 0.329 0.742
-.3389462 .4755872
par3 | .2423608 .2675854 0.906 0.365
-.282097 .7668187
opo2 | -.4849091 .1904389 -2.546 0.011
-.8581624 -.1116558
niv2 | .3749946 .1835695 2.043 0.041
.0152051 .7347842
niv3 | .4698068 .2847197 1.650 0.099
-.0882337 1.027847
---------+--------------------------------------------------------------------
_cut1 | -2.577238 .5710633 (Ancillary
parameters)
_cut2 | -1.238366 .5550304
_cut3 | .3049446 .5528824
------------------------------------------------------------------------------
Au-delà du premier trimestre
-> dgest=au delà d
Ordered logit estimates Number of obs
= 1010
LR chi2(33)
= 146.04
Prob > chi2
= 0.0000
Log likelihood = -1282.7242 Pseudo R2
= 0.0539
------------------------------------------------------------------------------
visite | Coef. Std. Err. z P>|z| [95%
Conf. Interval]
---------+--------------------------------------------------------------------
reg1 | -.7490638 .5091594 -1.471 0.141
-1.746998 .2488702
reg2 | -.3598288 .5004795 -0.719 0.472
-1.340751 .621093
reg3 | .1195843 .5829697 0.205 0.837
-1.023015 1.262184
reg4 | .4205269 .5480921 0.767 0.443
-.6537139 1.494768
reg5 | -.3537092 .4952623 -0.714 0.475
-1.324405 .616987
reg6 | .77283 .5071635 1.524 0.128
-.2211922 1.766852
reg7 | -.1653059 .5261344 -0.314 0.753
-1.19651 .8658986
reg8 | .0065952 .4787298 0.014 0.989
-.9316978 .9448883
reg10 | -.3286622 .6517024 -0.504 0.614
-1.605975 .9486511
reg11 | -.2213888 .5484449 -0.404 0.686
-1.296321 .8535436
res1 | 1.179758 .4870353 2.422 0.015
.2251865 2.13433
res2 | .6128086 .191326 3.203 0.001
.2378165 .9878008
eth1 | -.1288965 .417941 -0.308 0.758
-.9480458 .6902529
eth2 | .3940797 .3763238 1.047 0.295
-.3435013 1.131661
eth3 | -.2620856 .4456235 -0.588 0.556
-1.135492 .6113204
eth4 | .0800371 .3403318 0.235 0.814
-.587001 .7470752
eth5 | -1.219843 .4748905 -2.569 0.010
-2.150611 -.2890748
eth6 | -.5176373 .4512227 -1.147 0.251
-1.402018 .366743
eth8 | -.1916844 .3201089 -0.599 0.549
-.8190862 .4357175
eth9 | .2871483 .293171 0.979 0.327
-.2874564 .861753
soc1 | -.0232077 .2389905 -0.097 0.923
-.4916205 .4452051
soc2 | .1757634 .1708053 1.029 0.303
-.1590089 .5105357
rel2 | -.1624943 .2967052 -0.548 0.584
-.7440258 .4190371
rel3 | .7533953 .3375356 2.232 0.026
.0918377 1.414953
vie1 | 1.098153 .381977 2.875 0.004
.3494921 1.846814
vie2 | .1348087 .1328576 1.015 0.310
-.1255874 .3952048
age1 | .2733856 .1660594 1.646 0.100
-.0520849 .5988561
age3 | -.3551774 .1964341 -1.808 0.071
-.7401811 .0298263
par1 | -.3759916 .1662741 -2.261 0.024
-.7018828 -.0501003
par3 | .0452654 .2351291 0.193 0.847
-.4155791 .50611
opo2 | .3478032 .1781707 1.952 0.051
-.0014051 .6970114
niv2 | .1897907 .1439306 1.319 0.187
-.0923081 .4718895
niv3 | .3302432 .2733494 1.208 0.227
-.2055118 .8659983
---------+--------------------------------------------------------------------
_cut1 | -1.51811 .4961189 (Ancillary
parameters)
_cut2 | .0659767 .4917008
_cut3 | 1.419267 .4939962
------------------------------------------------------------------------------
->
Modèle 5
. by dgest: ologit visite reg1 reg2 reg3 reg4 reg5 reg6 reg7 reg8
reg9 reg10 re
> g11 res1 res2 eth1 eth2 eth3 eth4 eth5 eth6 eth8 eth9 soc1
soc2 rel2 rel3 vi
> e1 vie2 age1 age3 par1 par3 opo2 niv2 niv3 dis2 dis3 qvi1
qvi3
-> dgest=1er trime matsize too small; type -help matsize-
r(908);
. set matsize 150
. by dgest: ologit visite reg1 reg2 reg3 reg4 reg5 reg6 reg7 reg8
reg9 reg10 re
> g11 res1 res2 eth1 eth2 eth3 eth4 eth5 eth6 eth8 eth9 soc1
soc2 rel2 rel3 vi
> e1 vie2 age1 age3 par1 par3 opo2 niv2 niv3 dis2 dis3 qvi1
qvi3
Premier trimestre
-> dgest=1er trime
Ordered logit estimates Number of obs
= 832
LR chi2(37)
= 161.81
Prob > chi2
= 0.0000
Log likelihood = -755.62926 Pseudo R2
= 0.0967
------------------------------------------------------------------------------
visite | Coef. Std. Err. z P>|z| [95%
Conf. Interval]
---------+--------------------------------------------------------------------
reg1 | .1843666 .6988983 0.264 0.792
-1.185449 1.554182
reg2 | .5017596 .5608865 0.895 0.371
-.5975578 1.601077
reg4 | -.2324475 .6537288 -0.356 0.722
-1.513732 1.048837
reg5 | -.0972222 .5640628 -0.172 0.863
-1.202765 1.008321
reg6 | -.0465088 .5853374 -0.079 0.937
-1.193749 1.100731
reg7 | -.2238849 .5698045 -0.393 0.694
-1.340681 .8929114
reg8 | .0413458 .528771 0.078 0.938
-.9950264 1.077718
reg9 | .5748623 .6449829 0.891 0.373
-.6892809 1.839006
reg10 | .889438 .711746 1.250 0.211
-.5055586 2.284435
reg11 | 1.115838 .6283211 1.776 0.076
-.115649 2.347324
res1 | .5588456 .5178965 1.079 0.031
-.456213 1.573904
res2 | .561005 .2607882 2.151 0.281
.0498696 1.07214
eth1 | .0189637 .4935293 0.038 0.969
-.9483359 .9862634
eth2 | .0189643 .3967752 0.048 0.962
-.7587008 .7966294
eth3 | -.5943966 .4579001 -1.298 0.194
-1.491864 .3030712
eth4 | -.1506757 .395467 -0.381 0.703
-.9257768 .6244254
eth5 | -.6132659 .519844 -1.180 0.238
-1.632142 .4056097
eth6 | -.7134415 .4173498 -1.709 0.087
-1.531432 .1045491
eth8 | -.8258269 .4189538 -1.971 0.049
-1.646961 -.0046926
eth9 | -.0549872 .342674 -0.160 0.873
-.7266158 .6166414
soc1 | .25721 .2548811 1.009 0.313
-.2423477 .7567677
soc2 | .1279004 .2192956 0.583 0.560
-.3019111 .5577118
rel2 | .3506454 .3156659 1.111 0.267
-.2680483 .9693391
rel3 | -.5634928 .3901453 -1.444 0.149
-1.328164 .2011779
vie1 | .6617515 .3689376 1.794 0.073
-.0613529 1.384856
vie2 | .36267 .1777418 2.040 0.041
.0143025 .7110376
age1 | -.2044251 .2118357 -0.965 0.335
-.6196155 .2107654
age3 | -.1643713 .2314022 -0.710 0.478
-.6179113 .2891686
par1 | .0832618 .2108282 0.395 0.693
-.3299539 .4964775
par3 | .2416435 .2745369 0.880 0.379
-.2964389 .7797259
opo2 | -.5203107 .194023 -2.682 0.007
-.9005888 -.1400326
niv2 | .4198602 .1882342 2.231 0.026
.050928 .7887924
niv3 | .4708744 .2899514 1.624 0.040
-.0974198 1.039169
dis2 | .0433845 .2267068 0.191 0.848
-.4009527 .4877217
dis3 | -.2079863 .2344371 -0.887 0.375
-.6674746 .2515019
qvi1 | -.9915869 .1945565 -5.097 0.000
-1.372911 -.6102632
qvi3 | .463066 .1822948 2.540 0.011
.1057747 .8203573
---------+--------------------------------------------------------------------
_cut1 | -2.82294 .5770332 (Ancillary
parameters)
_cut2 | -1.412769 .5585588
_cut3 | .1986523 .5556922
------------------------------------------------------------------------------
Au-delà du premier trimestre
-> dgest=au delà d
Ordered logit estimates Number of obs
= 991
LR chi2(37)
= 281.77
Prob > chi2
= 0.0000
Log likelihood = -1191.2191 Pseudo R2
= 0.1058
------------------------------------------------------------------------------
visite | Coef. Std. Err. z P>|z| [95%
Conf. Interval]
---------+--------------------------------------------------------------------
reg1 | -.1755609 .6163081 -0.285 0.776
-1.383503 1.032381
reg2 | -.4293323 .5544053 -0.774 0.439
-1.515947 .6572821
reg3 | .1017655 .5987967 0.170 0.865
-1.071855 1.275385
reg4 | .3417424 .614794 0.556 0.578
-.8632316 1.546716
reg5 | .0850927 .5490414 0.155 0.877
-.9910086 1.161194
reg6 | 1.1116 .5711257 1.946 0.052
-.0077853 2.230986
reg7 | .4233753 .5806517 0.729 0.466
-.7146811 1.561432
reg8 | .1190301 .5366154 0.222 0.824
-.9327167 1.170777
reg9 | .1671123 .6553552 0.255 0.799
-1.11736 1.451585
reg11 | .1177761 .615885 0.191 0.848
-1.089336 1.324888
res1 | .773125 .549521 1.407 0.159
-.3039163 1.850166
res2 | .1771314 .2149992 0.824 0.410
-.2442593 .5985221
eth1 | -.2520184 .4420986 -0.570 0.569
-1.118516 .614479
eth2 | .2053069 .4016918 0.511 0.609
-.5819945 .9926083
eth3 | -.4731442 .4624943 -1.023 0.306
-1.379616 .4333279
eth4 | -.0453307 .3640118 -0.125 0.901
-.7587808 .6681193
eth5 | -1.259095 .4874606 -2.583 0.010
-2.214501 -.3036903
eth6 | -.4306593 .4609988 -0.934 0.350
-1.3342 .4728818
eth8 | -.0368531 .3290647 -0.112 0.911
-.6818081 .608102
eth9 | .211181 .3014338 0.701 0.484
-.3796184 .8019804
soc1 | -.0589432 .2475728 -0.238 0.812
-.5441769 .4262906
soc2 | .1048786 .1769268 0.593 0.553
-.2418915 .4516488
rel2 | .04663 .3170717 0.147 0.883
-.5748191 .668079
rel3 | .7386025 .3538681 2.087 0.037
.0450337 1.432171
vie1 | 1.204989 .3944469 3.055 0.002
.431887 1.97809
vie2 | .0485921 .1368368 0.355 0.723
-.2196032 .3167873
age1 | - .275687 .1731449 1.043 0.097
-.158789 .5199265
age3 | -.3337286 .2005784 -1.564 0.078
-.706855 .0793979
par1 | -.1635879 .1731526 -0.945 0.345
-.5029608 .1757851
par3 | .0871775 .2402013 0.363 0.717
-.3836084 .5579633
opo2 | .3589972 .183195 1.960 0.050
-.0000583 .7180528
niv2 | .1333303 .1469855 0.907 0.364
-.1547561 .4214167
niv3 | .2710432 .2803495 0.967 0.334
-.2784318 .8205182
dis2 | -.267635 .1957765 -1.367 0.172
-.6513499 .1160799
dis3 | -.3284527 .1822477 -1.802 0.072
-.6856517 .0287463
qvi1 | -1.200157 .1464795 -8.193 0.000
-1.487251 -.9130625
qvi3 | .9159681 .1889943 4.847 0.000
.545546 1.28639
---------+--------------------------------------------------------------------
_cut1 | -2.161863 .5674322 (Ancillary
parameters)
_cut2 | -.4176092 .5619805
_cut3 | 1.05433 .5630383
------------------------------------------------------------------------------
. log close
Depuis plusieurs décennies, les problèmes
de santé maternelle et infantile ne cessent de préoccuper toutes
les sociétés à travers le monde. Chaque année,
plus de 500 000 femmes dont la majorité vit dans les pays en
développement décèdent des suites d'une grossesse ou d'un
accouchement (OMS, 1999). Le Tchad n'est pas à l'abri de ce fléau
dévastant. Il est l'un des pays d'Afrique subsaharienne ayant un taux de
mortalité maternelle élevé largement au-dessus de la
moyenne africaine estimé à 800 cas pour 100 000 naissances
vivantes. La forte prévalence de la mortalité maternelle dans ce
pays s'explique en partie par la déperdition des soins prénatals.
Les études montrent que seulement 33% de femmes recourent aux soins de
santé modernes pendant la grossesse. Parmi la sous-population
utilisatrice des services prénatals, une large majorité ne
revient plus à la prochaine visite assurer la continuité des
soins. La déperdition des soins prénatals constitue ainsi un
blocage à la lutte contre la mortalité maternelle et
périnatale. Cette étude vise à identifier les facteurs
associés à ce phénomène de façon à
améliorer la santé maternelle et infantile au Tchad.
Since many decades, the societies worldwide are
unceasingly concerned with the problem of infant and maternal health. Every
year, more than 500 000 women living in underdeveloped countries die of
pregnancy or delivery (WHO, 1999). Chad is not exempted from this reality. It
is one of the subsaharian african countries having a high maternal mortality
rate, far above the average which is 800 cases per 100 000 children ever
born. This high maternal death rate is partly the consequence of some loss in
resorting antenatal care. This study has shown that only 33% of women resort to
modern health care during pregnancy. Among the subpopulation using the
antenatal services, a great majority does not come back for the next visits in
order to assure the continuity of care. Loss in care is therefore a hindrance
to the fight against maternal and perinatal death. The study aims at
identifying the factors that can likely explain this phenomenon, so as to
improve the infant and maternal health in Chad.
| 
