Sorry for imposing further but I could really use some help. Let me explain more.
I have data on incidences of poisonings in each of the 50 US states by quarter for 26 quarters. Each poisoning could result in one of
My hypothesis is that the new state policies have reduced the number of poisonings resulting in minor/ no adverse medical outcome [outcome==1] and increased the rate of poisonings resulting in more severe adverse medical outcomes [outcome==2]. Since the outcome, at the individual level is essentially binary (result in outcome 1 or 2), I have been recommended a *blocked/ grouped* logit to test if the policy increased the probability of the worse outcomes and reduced the probability of the more minor adverse outcome [outcome==1]. For reasons of past literature, I included fixed effects for each state, quarter and state specific linear and quadratic time trends. I run the following:
However, ideally I want to run this on state-population normalized data [variable poisonings_popstd]
I have data on incidences of poisonings in each of the 50 US states by quarter for 26 quarters. Each poisoning could result in one of
2 outcomes - no/ minor adverse medical outcome [outcome==1] or severe adverse medical outcome [outcome=2]. So the outcome is essentially binary - outcome 1 or outcome 2
. I want to evaluate the impact of a certain government policy adopted by a subset of states [identified in the data below with variable treated==1] poisonings.
I also have, separately from the US Census, the population of each state (variable 'pop' below). My data looks as follows:Code:
* Example generated by -dataex-. To install: ssc install dataex clear input str5 state float qtr long poisonings float(total poisonings_popstd outcome post) byte treated long pop float(x1 x2 x3 x4 x5 x6) "AK" 1 20 27 2.769937 1 0 1 722038 7.733333 27.7 4.545096 28.878407 .52014977 .08111796 "AK" 1 7 27 .9694781 2 0 1 722038 7.733333 27.7 4.545096 28.878407 .52014977 .08111796 "AK" 2 20 29 2.769937 1 0 1 722038 7.566667 27.7 4.545096 28.878407 .52014977 .08111796 "AK" 2 9 29 1.2464718 2 0 1 722038 7.566667 27.7 4.545096 28.878407 .52014977 .08111796 "AK" 3 27 35 3.7394154 1 0 1 722038 7.5 27.7 4.545096 28.878407 .52014977 .08111796 "AK" 3 8 35 1.1079749 2 0 1 722038 7.5 27.7 4.545096 28.878407 .52014977 .08111796 "AK" 4 18 32 2.4929435 1 0 1 722038 7.466667 27.7 4.545096 28.878407 .52014977 .08111796 "AK" 4 14 32 1.938956 2 0 1 722038 7.466667 27.7 4.545096 28.878407 .52014977 .08111796 "AK" 5 11 26 1.506026 2 0 1 730399 7.333333 27.7 4.6983337 29.19467 .5209735 .0854725 "AK" 5 15 26 2.053672 1 0 1 730399 7.333333 27.7 4.6983337 29.19467 .5209735 .0854725 "AK" 6 6 29 .8214688 2 0 1 730399 7.166667 27.7 4.6983337 29.19467 .5209735 .0854725 "AK" 6 23 29 3.148964 1 0 1 730399 7.166667 27.7 4.6983337 29.19467 .5209735 .0854725 "AK" 7 13 17 1.779849 1 0 1 730399 7.033333 27.7 4.6983337 29.19467 .5209735 .0854725 "AK" 7 4 17 .54764587 2 0 1 730399 7.033333 27.7 4.6983337 29.19467 .5209735 .0854725 "AK" 8 18 23 2.4644065 1 0 1 730399 7 27.7 4.6983337 29.19467 .5209735 .0854725 "AK" 8 5 23 .6845573 2 0 1 730399 7 27.7 4.6983337 29.19467 .5209735 .0854725 "AK" 9 16 18 2.1708307 1 0 1 737045 7 27.7 4.786403 29.4967 .5228226 .08950587 "AK" 9 2 18 .27135384 2 0 1 737045 7 27.7 4.786403 29.4967 .5228226 .08950587 "AK" 10 10 19 1.3567692 2 0 1 737045 7 27.7 4.786403 29.4967 .5228226 .08950587 "AK" 10 9 19 1.2210923 1 0 1 737045 7 27.7 4.786403 29.4967 .5228226 .08950587 "AK" 11 18 24 2.442185 1 0 1 737045 7 27.7 4.786403 29.4967 .5228226 .08950587 "AK" 11 6 24 .8140616 2 0 1 737045 7 27.7 4.786403 29.4967 .5228226 .08950587 "AK" 12 18 28 2.442185 1 0 1 737045 7 27.7 4.786403 29.4967 .5228226 .08950587 "AK" 12 10 28 1.3567692 2 0 1 737045 7 27.7 4.786403 29.4967 .5228226 .08950587 "AK" 13 7 12 .9506904 1 0 1 736307 7 27.7 4.795406 29.827 .5233248 .09415574 "AK" 13 5 12 .6790646 2 0 1 736307 7 27.7 4.795406 29.827 .5233248 .09415574 "AK" 14 16 24 2.1730065 1 0 1 736307 7 27.7 4.795406 29.827 .5233248 .09415574 "AK" 14 8 24 1.0865033 2 0 1 736307 7 27.7 4.795406 29.827 .5233248 .09415574 "AK" 15 18 24 2.4446325 1 0 1 736307 6.866667 27.7 4.795406 29.827 .5233248 .09415574 "AK" 15 6 24 .8148775 2 0 1 736307 6.866667 27.7 4.795406 29.827 .5233248 .09415574 "AK" 16 16 25 2.1730065 1 0 1 736307 6.6 27.7 4.795406 29.827 .5233248 .09415574 "AK" 16 9 25 1.2223163 2 0 1 736307 6.6 27.7 4.795406 29.827 .5233248 .09415574 "AK" 17 23 29 3.1184454 1 0 1 737547 6.5 27.7 4.818702 30.10442 .5234001 .09878014 "AK" 17 6 29 .8135075 2 0 1 737547 6.5 27.7 4.818702 30.10442 .5234001 .09878014 "AK" 18 12 27 1.627015 1 0 1 737547 6.5 27.7 4.818702 30.10442 .5234001 .09878014 "AK" 18 15 27 2.0337687 2 0 1 737547 6.5 27.7 4.818702 30.10442 .5234001 .09878014 "AK" 19 6 13 .8135075 2 0 1 737547 6.5 27.7 4.818702 30.10442 .5234001 .09878014 "AK" 19 7 13 .949092 1 0 1 737547 6.5 27.7 4.818702 30.10442 .5234001 .09878014 "AK" 20 3 23 .40675375 2 0 1 737547 6.633333 27.7 4.818702 30.10442 .5234001 .09878014 "AK" 20 20 23 2.7116916 1 0 1 737547 6.633333 27.7 4.818702 30.10442 .5234001 .09878014 "AK" 21 19 31 2.56236 1 0 1 741504 6.766667 27.7 4.911483 30.41499 .5231637 .10406608 "AK" 21 12 31 1.6183325 2 0 1 741504 6.766667 27.7 4.911483 30.41499 .5231637 .10406608 "AK" 22 8 18 1.0788883 2 0 1 741504 6.866667 27.7 4.911483 30.41499 .5231637 .10406608 "AK" 22 10 18 1.3486104 1 0 1 741504 6.866667 27.7 4.911483 30.41499 .5231637 .10406608 "AK" 23 5 27 .6743052 2 0 1 741504 7 27.7 4.911483 30.41499 .5231637 .10406608 "AK" 23 22 27 2.966943 1 0 1 741504 7 27.7 4.911483 30.41499 .5231637 .10406608 "AK" 24 9 20 1.2137494 2 0 1 741504 7 27.7 4.911483 30.41499 .5231637 .10406608 "AK" 24 11 20 1.4834714 1 0 1 741504 7 27.7 4.911483 30.41499 .5231637 .10406608 "AK" 25 16 26 2.1627877 1 0 1 739786 7.033333 27.7 . . . . "AK" 25 10 26 1.3517423 2 0 1 739786 7.033333 27.7 . . . . "AK" 26 20 31 2.7034845 1 0 1 739786 7.133333 27.7 . . . . "AK" 26 11 31 1.4869165 2 0 1 739786 7.133333 27.7 . . . . "AL" 1 127 191 2.646476 1 0 0 4798834 10.166667 31 26.847376 28.9922 .4852006 .14009614 "AL" 1 64 191 1.3336573 2 0 0 4798834 10.166667 31 26.847376 28.9922 .4852006 .14009614 "AL" 2 120 183 2.5006075 1 0 0 4798834 10 31 26.847376 28.9922 .4852006 .14009614 "AL" 2 63 183 1.312819 2 0 0 4798834 10 31 26.847376 28.9922 .4852006 .14009614 "AL" 3 62 172 1.2919805 2 0 0 4798834 9.666667 31 26.847376 28.9922 .4852006 .14009614 "AL" 3 110 172 2.2922235 1 0 0 4798834 9.666667 31 26.847376 28.9922 .4852006 .14009614 "AL" 4 52 161 1.0835966 2 0 0 4798834 8.633333 31 26.847376 28.9922 .4852006 .14009614 "AL" 4 109 161 2.2713852 1 0 0 4798834 8.633333 31 26.847376 28.9922 .4852006 .14009614 "AL" 5 66 171 1.370556 2 0 0 4815564 8 31 26.951054 29.14335 .4851257 .14522338 "AL" 5 105 171 2.18043 1 0 0 4815564 8 31 26.951054 29.14335 .4851257 .14522338 "AL" 6 65 198 1.34979 2 0 0 4815564 8.2 31 26.951054 29.14335 .4851257 .14522338 "AL" 6 133 198 2.761878 1 0 0 4815564 8.2 31 26.951054 29.14335 .4851257 .14522338 "AL" 7 127 202 2.637282 1 0 0 4815564 8.066667 31 26.951054 29.14335 .4851257 .14522338 "AL" 7 75 202 1.55745 2 0 0 4815564 8.066667 31 26.951054 29.14335 .4851257 .14522338 "AL" 8 123 196 2.554218 1 0 0 4815564 7.666667 31 26.951054 29.14335 .4851257 .14522338 "AL" 8 73 196 1.515918 2 0 0 4815564 7.666667 31 26.951054 29.14335 .4851257 .14522338 "AL" 9 128 193 2.649851 1 0 0 4830460 7.4 31 27.068136 29.300863 .4850109 .14920263 "AL" 9 65 193 1.3456275 2 0 0 4830460 7.4 31 27.068136 29.300863 .4850109 .14920263 "AL" 10 104 168 2.1530042 1 0 0 4830460 7.1 31 27.068136 29.300863 .4850109 .14920263 "AL" 10 64 168 1.3249255 2 0 0 4830460 7.1 31 27.068136 29.300863 .4850109 .14920263 "AL" 11 62 169 1.2835217 2 0 0 4830460 7.133333 31 27.068136 29.300863 .4850109 .14920263 "AL" 11 107 169 2.21511 1 0 0 4830460 7.133333 31 27.068136 29.300863 .4850109 .14920263 "AL" 12 93 170 1.9252825 1 0 0 4830460 7.233333 31 27.068136 29.300863 .4850109 .14920263 "AL" 12 77 170 1.594051 2 0 0 4830460 7.233333 31 27.068136 29.300863 .4850109 .14920263 "AL" 13 87 160 1.7965997 1 0 0 4842481 7.233333 31 27.15205 29.434875 .4848089 .15354267 "AL" 13 73 160 1.5074917 2 0 0 4842481 7.233333 31 27.15205 29.434875 .4848089 .15354267 "AL" 14 76 159 1.5694435 2 0 0 4842481 7 31 27.15205 29.434875 .4848089 .15354267 "AL" 14 83 159 1.7139975 1 0 0 4842481 7 31 27.15205 29.434875 .4848089 .15354267 "AL" 15 75 178 1.548793 2 0 0 4842481 6.6 31 27.15205 29.434875 .4848089 .15354267 "AL" 15 103 178 2.127009 1 0 0 4842481 6.6 31 27.15205 29.434875 .4848089 .15354267 "AL" 16 65 145 1.3422872 2 0 0 4842481 6.233333 31 27.15205 29.434875 .4848089 .15354267 "AL" 16 80 145 1.6520457 1 0 0 4842481 6.233333 31 27.15205 29.434875 .4848089 .15354267 "AL" 17 64 116 1.3187284 1 0 0 4853160 6.1 31 27.26294 29.60861 .484659 .1574272 "AL" 17 52 116 1.0714668 2 0 0 4853160 6.1 31 27.26294 29.60861 .484659 .1574272 "AL" 18 47 131 .9684412 2 0 0 4853160 6.166667 31 27.26294 29.60861 .484659 .1574272 "AL" 18 84 131 1.730831 1 0 0 4853160 6.166667 31 27.26294 29.60861 .484659 .1574272 "AL" 19 60 138 1.236308 2 0 0 4853160 6.1 31 27.26294 29.60861 .484659 .1574272 "AL" 19 78 138 1.6072003 1 0 0 4853160 6.1 31 27.26294 29.60861 .484659 .1574272 "AL" 20 61 139 1.256913 2 0 0 4853160 6 31 27.26294 29.60861 .484659 .1574272 "AL" 20 78 139 1.6072003 1 0 0 4853160 6 31 27.26294 29.60861 .484659 .1574272 "AL" 21 50 138 1.0278031 2 0 0 4864745 5.966667 31 27.34045 29.740936 .4843596 .1613207 "AL" 21 88 138 1.8089335 1 0 0 4864745 5.966667 31 27.34045 29.740936 .4843596 .1613207 "AL" 22 67 145 1.377256 2 0 0 4864745 5.833333 31 27.34045 29.740936 .4843596 .1613207 "AL" 22 78 145 1.6033728 1 0 0 4864745 5.833333 31 27.34045 29.740936 .4843596 .1613207 "AL" 23 70 124 1.4389243 1 0 0 4864745 5.833333 31 27.34045 29.740936 .4843596 .1613207 "AL" 23 54 124 1.1100273 2 0 0 4864745 5.833333 31 27.34045 29.740936 .4843596 .1613207 "AL" 24 85 134 1.7472652 1 0 0 4864745 5.8 31 27.34045 29.740936 .4843596 .1613207 "AL" 24 49 134 1.0072471 2 0 0 4864745 5.8 31 27.34045 29.740936 .4843596 .1613207 end
My hypothesis is that the new state policies have reduced the number of poisonings resulting in minor/ no adverse medical outcome [outcome==1] and increased the rate of poisonings resulting in more severe adverse medical outcomes [outcome==2]. Since the outcome, at the individual level is essentially binary (result in outcome 1 or 2), I have been recommended a *blocked/ grouped* logit to test if the policy increased the probability of the worse outcomes and reduced the probability of the more minor adverse outcome [outcome==1]. For reasons of past literature, I included fixed effects for each state, quarter and state specific linear and quadratic time trends. I run the following:
Code:
glm poisonings post treated x1 x2 x3 x4 x5 x6 state_share_rural_2010 md_100000 pa_1000
> 00 rn_100000 i.qtr i.stateFIPS i.stateFIPS#(c.qtr c.qtrsq) if outcome==2, family(binom
> ial total) link(logit) vce(cluster state)
note: 53.stateFIPS omitted because of collinearity
note: 54.stateFIPS omitted because of collinearity
note: 55.stateFIPS omitted because of collinearity
note: 55.stateFIPS#c.qtr omitted because of collinearity
note: 55.stateFIPS#c.qtrsq omitted because of collinearity
Iteration 0: log pseudolikelihood = -3591.7962
Iteration 1: log pseudolikelihood = -3588.9357
Iteration 2: log pseudolikelihood = -3588.9354
Generalized linear models No. of obs = 1,128
Optimization : ML Residual df = 1,097
Scale parameter = 1
Deviance = 1364.558325 (1/df) Deviance = 1.2439
Pearson = 1350.134022 (1/df) Pearson = 1.230751
Variance function: V(u) = u*(1-u/total) [Binomial]
Link function : g(u) = ln(u/(total-u)) [Logit]
AIC = 6.418325
Log pseudolikelihood = -3588.935448 BIC = -6345.379
(Std. Err. adjusted for 47 clusters in state)
----------------------------------------------------------------------------------------
| Robust
poisonings | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-----------------------+----------------------------------------------------------------
post | .1280374 .0449471 2.85 0.004 .0399427 .2161321
treated | 9.455857 18.72428 0.51 0.614 -27.24306 46.15478
x1 | -.0317194 .023618 -1.34 0.179 -.0780098 .014571
x2 | -.5660772 1.021753 -0.55 0.580 -2.568676 1.436522
x3 | .0800275 .5061473 0.16 0.874 -.9120029 1.072058
x4 | -.0137807 .307849 -0.04 0.964 -.6171536 .5895922
x5 | 122.5292 101.0741 1.21 0.225 -75.57246 320.6308
x6 | 44.02676 24.5068 1.80 0.072 -4.005688 92.0592
state_share_rural_2010 | -10.30704 65.50204 -0.16 0.875 -138.6887 118.0746
md_100000 | -.0520859 .094167 -0.55 0.580 -.2366498 .1324779
pa_100000 | .0444265 .2285538 0.19 0.846 -.4035307 .4923837
rn_100000 | .0143391 .0295121 0.49 0.627 -.0435036 .0721817
|
qtr |
2 | -.0681311 .0350371 -1.94 0.052 -.1368026 .0005403
3 | -.0792729 .0404783 -1.96 0.050 -.1586088 .0000631
4 | -.1231814 .0446144 -2.76 0.006 -.2106239 -.0357388
5 | -.3408823 .1485142 -2.30 0.022 -.6319648 -.0497997
6 | -.376968 .152566 -2.47 0.013 -.6759919 -.0779441
7 | -.3821582 .1491666 -2.56 0.010 -.6745193 -.0897971
8 | -.4553765 .1579642 -2.88 0.004 -.7649807 -.1457722
9 | -.593276 .2767458 -2.14 0.032 -1.135688 -.0508643
10 | -.5681158 .2796033 -2.03 0.042 -1.116128 -.0201034
11 | -.6535904 .2897996 -2.26 0.024 -1.221587 -.0855936
12 | -.6966991 .2820733 -2.47 0.014 -1.249553 -.1438457
13 | -.9303921 .4069777 -2.29 0.022 -1.728054 -.1327305
14 | -.9062193 .4147891 -2.18 0.029 -1.719191 -.0932476
15 | -.9076593 .4173466 -2.17 0.030 -1.725644 -.089675
16 | -.8987763 .4118481 -2.18 0.029 -1.705984 -.0915689
17 | -1.132076 .5379077 -2.10 0.035 -2.186356 -.0777966
18 | -1.035746 .547678 -1.89 0.059 -2.109175 .0376827
19 | -1.055255 .5546635 -1.90 0.057 -2.142376 .0318653
20 | -1.113239 .5579486 -2.00 0.046 -2.206798 -.0196796
21 | -1.26025 .6798281 -1.85 0.064 -2.592689 .0721882
22 | -1.202498 .6817646 -1.76 0.078 -2.538732 .1337362
23 | -1.181239 .6835069 -1.73 0.084 -2.520888 .1584101
24 | -1.187128 .7014095 -1.69 0.091 -2.561865 .1876099
|
stateFIPS |
2 | -8.947468 15.81543 -0.57 0.572 -39.94515 22.05021
4 | -12.0106 25.66544 -0.47 0.640 -62.31393 38.29274
5 | -6.085038 14.60429 -0.42 0.677 -34.70891 22.53884
6 | -11.44933 26.53333 -0.43 0.666 -63.4537 40.55503
8 | -3.529745 8.667129 -0.41 0.684 -20.51701 13.45752
9 | -4.957952 18.50464 -0.27 0.789 -41.22638 31.31047
10 | -9.679517 27.42826 -0.35 0.724 -63.43792 44.07889
12 | -1.411309 12.39931 -0.11 0.909 -25.7135 22.89089
13 | -8.957343 23.76524 -0.38 0.706 -55.53636 37.62167
15 | 2.410176 19.07879 0.13 0.899 -34.98357 39.80392
16 | .5550814 5.612196 0.10 0.921 -10.44462 11.55478
17 | -10.10562 27.64226 -0.37 0.715 -64.28347 44.07222
18 | -7.997299 22.59134 -0.35 0.723 -52.27551 36.28091
19 | 1.256184 5.697489 0.22 0.825 -9.91069 12.42306
20 | -2.694958 4.422801 -0.61 0.542 -11.36349 5.973572
21 | -6.169956 13.26489 -0.47 0.642 -32.16867 19.82876
22 | -6.391388 21.14652 -0.30 0.762 -47.8378 35.05502
23 | 4.271387 23.94119 0.18 0.858 -42.65247 51.19525
24 | -6.223166 22.78421 -0.27 0.785 -50.87939 38.43306
25 | -5.287871 21.46285 -0.25 0.805 -47.35428 36.77854
26 | 2.713903 4.803896 0.56 0.572 -6.70156 12.12937
27 | -12.14202 22.93662 -0.53 0.597 -57.09697 32.81292
28 | -.4793367 2.3538 -0.20 0.839 -5.0927 4.134027
29 | 1.934788 6.445643 0.30 0.764 -10.69844 14.56802
30 | 2.677419 18.53608 0.14 0.885 -33.65263 39.00747
31 | -6.732864 10.03005 -0.67 0.502 -26.3914 12.92567
32 | -9.16496 26.75222 -0.34 0.732 -61.59834 43.26842
33 | -8.881691 14.92266 -0.60 0.552 -38.12956 20.36618
34 | -5.947119 26.02535 -0.23 0.819 -56.95588 45.06164
35 | -9.350599 18.14553 -0.52 0.606 -44.91518 26.21399
36 | -4.148707 17.65852 -0.23 0.814 -38.75877 30.46136
37 | -1.345194 4.815853 -0.28 0.780 -10.78409 8.093704
39 | -6.138378 22.08242 -0.28 0.781 -49.41913 37.14237
40 | -6.719478 15.08482 -0.45 0.656 -36.28517 22.84622
41 | 2.539168 3.731686 0.68 0.496 -4.774802 9.853138
42 | -4.229331 16.34496 -0.26 0.796 -36.26487 27.80621
44 | -6.861775 26.88413 -0.26 0.799 -59.5537 45.83015
45 | -10.46153 20.59378 -0.51 0.611 -50.8246 29.90154
46 | -10.28722 16.97919 -0.61 0.545 -43.56582 22.99138
47 | -6.669694 18.00786 -0.37 0.711 -41.96446 28.62507
48 | -10.08412 25.88989 -0.39 0.697 -60.82737 40.65912
49 | -11.6419 27.24898 -0.43 0.669 -65.04893 41.76512
51 | -8.827182 18.90776 -0.47 0.641 -45.88571 28.23135
53 | 0 (omitted)
54 | 0 (omitted)
55 | 0 (omitted)
|
stateFIPS#c.qtr |
1 | .0873125 .0090174 9.68 0.000 .0696387 .1049863
2 | .0062401 .0516663 0.12 0.904 -.0950241 .1075043
4 | .0232368 .0121039 1.92 0.055 -.0004864 .0469601
5 | -.0140137 .0073818 -1.90 0.058 -.0284817 .0004543
6 | .0474572 .0153308 3.10 0.002 .0174094 .0775051
8 | .0428852 .0263179 1.63 0.103 -.0086969 .0944674
9 | .0875877 .0138127 6.34 0.000 .0605154 .1146601
10 | .0494902 .0226979 2.18 0.029 .0050031 .0939773
12 | -.0365217 .0119252 -3.06 0.002 -.0598947 -.0131488
13 | -.0292258 .0121902 -2.40 0.017 -.0531182 -.0053334
15 | -.0103332 .0455048 -0.23 0.820 -.0995209 .0788545
16 | .1305838 .0095613 13.66 0.000 .1118441 .1493235
17 | .0688074 .0069859 9.85 0.000 .0551152 .0824995
18 | .043609 .0060178 7.25 0.000 .0318142 .0554037
19 | .0700069 .0107145 6.53 0.000 .0490069 .0910069
20 | .0670102 .0134287 4.99 0.000 .0406904 .0933301
21 | -.0177537 .0126121 -1.41 0.159 -.042473 .0069656
22 | .1274598 .0128937 9.89 0.000 .1021886 .1527311
23 | .1445257 .0190443 7.59 0.000 .1071996 .1818519
24 | .0023594 .0252601 0.09 0.926 -.0471494 .0518682
25 | -.0220327 .027342 -0.81 0.420 -.0756221 .0315566
26 | .0698199 .008729 8.00 0.000 .0527114 .0869283
27 | .0593758 .0098976 6.00 0.000 .0399768 .0787747
28 | .0904343 .0075578 11.97 0.000 .0756213 .1052474
29 | .0323497 .0063185 5.12 0.000 .0199657 .0447338
30 | .0640228 .0149398 4.29 0.000 .0347413 .0933043
31 | .1089145 .0132758 8.20 0.000 .0828945 .1349346
32 | .0247509 .0193151 1.28 0.200 -.013106 .0626079
33 | .0090151 .0174771 0.52 0.606 -.0252395 .0432696
34 | .0532562 .0139683 3.81 0.000 .0258788 .0806335
35 | -.0146453 .0179873 -0.81 0.416 -.0498997 .0206092
36 | -.0012594 .0227893 -0.06 0.956 -.0459255 .0434068
37 | .046556 .0144417 3.22 0.001 .0182508 .0748612
39 | .0157902 .0124939 1.26 0.206 -.0086973 .0402778
40 | -.0534207 .0100618 -5.31 0.000 -.0731415 -.0336999
41 | -.0450526 .0146912 -3.07 0.002 -.0738469 -.0162583
42 | .0743016 .0164902 4.51 0.000 .0419815 .1066217
44 | .0563772 .0083841 6.72 0.000 .0399447 .0728098
45 | .0279634 .0201218 1.39 0.165 -.0114745 .0674013
46 | .1439447 .0710833 2.03 0.043 .0046241 .2832654
47 | .0001434 .0143339 0.01 0.992 -.0279506 .0282374
48 | .0460944 .0084858 5.43 0.000 .0294626 .0627262
49 | .041073 .0182258 2.25 0.024 .005351 .076795
51 | .0420577 .0182573 2.30 0.021 .0062741 .0778413
53 | .0221912 .0212663 1.04 0.297 -.0194901 .0638724
54 | .0086252 .0130878 0.66 0.510 -.0170265 .0342768
55 | 0 (omitted)
|
stateFIPS#c.qtrsq |
1 | -.0022872 .0003433 -6.66 0.000 -.0029602 -.0016143
2 | -.0002177 .0014478 -0.15 0.880 -.0030554 .00262
4 | -.0005262 .000264 -1.99 0.046 -.0010436 -8.80e-06
5 | .0017617 .0002188 8.05 0.000 .0013329 .0021906
6 | -.001821 .0002215 -8.22 0.000 -.0022551 -.0013869
8 | -.0018612 .0003925 -4.74 0.000 -.0026304 -.001092
9 | -.0024705 .000478 -5.17 0.000 -.0034074 -.0015336
10 | -.0013925 .000395 -3.53 0.000 -.0021667 -.0006184
12 | .0007492 .0004226 1.77 0.076 -.000079 .0015774
13 | .0010488 .0003898 2.69 0.007 .0002848 .0018128
15 | .0002051 .0008639 0.24 0.812 -.0014881 .0018982
16 | -.0033215 .0002989 -11.11 0.000 -.0039074 -.0027357
17 | -.0016999 .0001638 -10.38 0.000 -.0020209 -.001379
18 | -.0006631 .0002067 -3.21 0.001 -.0010682 -.0002581
19 | -.0012122 .0001109 -10.93 0.000 -.0014295 -.0009949
20 | -.0016441 .000369 -4.46 0.000 -.0023673 -.0009209
21 | .0005559 .0004014 1.39 0.166 -.0002307 .0013426
22 | -.0024825 .0003398 -7.31 0.000 -.0031485 -.0018165
23 | -.0048119 .0004619 -10.42 0.000 -.0057173 -.0039066
24 | -.0003339 .0004897 -0.68 0.495 -.0012937 .0006259
25 | .0006504 .0004045 1.61 0.108 -.0001424 .0014432
26 | -.002681 .0001529 -17.54 0.000 -.0029806 -.0023814
27 | -.0012435 .0002437 -5.10 0.000 -.001721 -.0007659
28 | -.0027013 .000447 -6.04 0.000 -.0035775 -.0018251
29 | -.0012331 .0002127 -5.80 0.000 -.00165 -.0008162
30 | -.0023044 .0002618 -8.80 0.000 -.0028176 -.0017912
31 | -.0026962 .0002079 -12.97 0.000 -.0031036 -.0022887
32 | -.0007269 .0004593 -1.58 0.114 -.0016271 .0001734
33 | -.0000602 .0003107 -0.19 0.846 -.0006691 .0005488
34 | -.0017158 .0003741 -4.59 0.000 -.0024489 -.0009827
35 | .0003067 .0004568 0.67 0.502 -.0005886 .001202
36 | -.0001027 .0005236 -0.20 0.844 -.0011288 .0009235
37 | -.0011671 .0003314 -3.52 0.000 -.0018166 -.0005176
39 | -.0008741 .0003671 -2.38 0.017 -.0015936 -.0001547
40 | .0007359 .0004954 1.49 0.137 -.0002351 .0017068
41 | .0015358 .0006019 2.55 0.011 .000356 .0027156
42 | -.0016836 .0003356 -5.02 0.000 -.0023413 -.0010259
44 | -.001232 .0003575 -3.45 0.001 -.0019326 -.0005314
45 | .0001419 .0004377 0.32 0.746 -.000716 .0009998
46 | -.0039741 .0009312 -4.27 0.000 -.0057992 -.002149
47 | .0006898 .0004325 1.59 0.111 -.000158 .0015376
48 | -.0008588 .0002376 -3.61 0.000 -.0013245 -.0003931
49 | -.0007886 .0004077 -1.93 0.053 -.0015876 .0000104
51 | -.001024 .0004956 -2.07 0.039 -.0019953 -.0000526
53 | -.0005743 .0004112 -1.40 0.163 -.0013802 .0002316
54 | -.0010522 .0003709 -2.84 0.005 -.0017792 -.0003252
55 | 0 (omitted)
|
_cons | -51.25055 47.68432 -1.07 0.282 -144.7101 42.20899
---------------------------------------------------------------------------------------
that I got by
converting the number of poisonings to rates of poisonings per 100,000 persons. Population standardized estimates are better to compare (changes in) rates across states with otherwise very different population sizes
. How can I do that?
I could really appreciate your help with this.
Sincerely,
Sumedha.
I could really appreciate your help with this.
Sincerely,
Sumedha.
Comment