Diabetes

Exploratory Data analysis

Load Libraries

Functions

myprint=function(x, lab="") {print(x)%>%kbl(caption=lab)%>%kable_classic(html_font="Cambria")}

mymiss=function(var, lab=""){
temp=rep(0,9)
for (i in 1:9){
  temp[i]=sum(is.na(var[total$Year==i+2010]))/length(var[total$Year==i+2010])}
  myseq=seq(2011,2019)
  temp=as.data.frame(cbind(myseq,temp))
  colnames(temp)=c("Year", "Var")
  
  mygg=ggplot(temp,aes(x=Year,y=Var))+
    geom_point()+
    geom_smooth()+
    geom_text(aes(label=round(Var,3)),hjust=1.1, vjust=.5, size=3)+
    ylab('% Missing')+
    xlab('Year')+
    theme(strip.background = element_blank(),strip.text.y = element_blank())+
  scale_x_continuous(name="Year", breaks=c(2011,2012,2013,2014,2015,2016,2017,2018,2019))+
    ggtitle(lab)

  print(mygg)
  
}

Load Data

These are the SAS transport files provided by BRFSS.

y2019=read.xport("Y2019.XPT")
y2018=read.xport("Y2018.XPT")
y2017=read.xport("Y2017.XPT")
y2016=read.xport("Y2016.XPT")
y2015=read.xport("Y2015.XPT")
y2014=read.xport("Y2014.XPT")
y2013=read.xport("Y2013.XPT")
y2012=read.xport("Y2012.XPT")
y2011=read.xport("Y2011.XPT")

Diabetes Only Data

y2011=y2011[y2011$DIABETE3==1 & as.numeric(y2011$X_AGE_G)>3,]
y2012=y2012[y2012$DIABETE3==1 & as.numeric(y2012$X_AGE_G)>3,]
y2013=y2013[y2013$DIABETE3==1 & as.numeric(y2013$X_AGE_G)>3,]
y2014=y2014[y2014$DIABETE3==1 & as.numeric(y2014$X_AGE_G)>3,]
y2015=y2015[y2015$DIABETE3==1 & as.numeric(y2015$X_AGE_G)>3,]
y2016=y2016[y2016$DIABETE3==1 & as.numeric(y2016$X_AGE_G)>3,]
y2017=y2017[y2017$DIABETE3==1 & as.numeric(y2017$X_AGE_G)>3,]
y2018=y2018[y2018$DIABETE3==1 & as.numeric(y2018$X_AGE_G)>3,]
y2019=y2019[y2019$DIABETE4==1 & as.numeric(y2019$X_AGE_G)>3,]

Recode Updated Questions

These questions are either identical or map one-to-one but have a different number at the end of the variable

y2019$DIABAGE2=y2019$DIABAGE3 #identical question in codebook year over year
y2019$INSULIN=y2019$INSULIN1 #identical question in codebook year over year
y2019$EYEEXAM=y2019$EYEEXAM1 #same question
y2018$EYEEXAM=y2018$EYEEXAM1 #same question
y2019$SEX=y2019$SEXVAR  #gender questions are non-standard but standardizable
y2018$SEX=y2018$SEX1 #same question
y2011$EMPLOY1=y2011$EMPLOY  #same question
y2012$EMPLOY1=y2012$EMPLOY #same question
y2011$X_RACE_G1=y2011$X_RACE_G #same question
y2012$X_RACE_G1=y2012$X_RACE_G #same Question

Reduce Data

Select the variables appropriate for the analysis.

myvars=c("IMONTH","X_AGE_G","X_RACE_G1","SEX",
         "MARITAL","INCOME2","EDUCA","EMPLOY1","GENHLTH",
         "HLTHPLN1","PERSDOC2", "CHECKUP1", "MEDCOST","DIABAGE2",
         "DIABEDU", "INSULIN", "BLDSUGAR", "FEETCHK", "DOCTDIAB",
         "CHKHEMO3", "DIABEYE", "EYEEXAM",
         "X_STATE", "X_STSTR" ,  "X_LLCPWT")

y2019=y2019[myvars]
y2018=y2018[myvars]
y2017=y2017[myvars]
y2016=y2016[myvars]
y2015=y2015[myvars]
y2014=y2014[myvars]
y2013=y2013[myvars]
y2012=y2012[myvars]
y2011=y2011[myvars]

Code Years

Log the year of the survey as part of the data set.

y2019$Year=rep(2019, nrow(y2019))
y2018$Year=rep(2018, nrow(y2018))
y2017$Year=rep(2017, nrow(y2017))
y2016$Year=rep(2016, nrow(y2016))
y2015$Year=rep(2015, nrow(y2015))
y2014$Year=rep(2014, nrow(y2014))
y2013$Year=rep(2013, nrow(y2013))
y2012$Year=rep(2012, nrow(y2012))
y2011$Year=rep(2011, nrow(y2011))

Combine & Write .csv

Combine all years of data. Also, write the .csv for future use.

total=rbind(y2011,y2012,y2013,y2014,y2015,y2016,y2017,y2018,y2019)
write.csv(total, "diabetes.csv", row.names=FALSE)
rm(y2011)
rm(y2012)
rm(y2013)
rm(y2014)
rm(y2015)
rm(y2016)
rm(y2017)
rm(y2018)
rm(y2019)
total=read.csv("diabetes.csv")
total=total[!is.na(total$X_STSTR),] #drop 393 observations

Clean Dependent Variables

Blood Sugar

About how often do you check your blood for glucose or sugar?

0=Never, Refused, Don’t Know, 1=All Other Frequencies

#########################Blood Sugar###########################
myprint(table(total$BLDSUGAR))

## 
##   101   102   103   104   105   106   107   108   109   110   111   112   113 
## 57317 39002 19240 10401  2417  1436   432   463    42   197     4    42     1 
##   114   115   116   118   120   121   122   123   124   125   128   130   140 
##     9    14     1     1    10     2     1     2     2     6     1    15     2 
##   148   150   160   175   182   188   190   198   199   201   202   203   204 
##     1     1     2     1     1     1     1   155    22 11683  8457  9408  3932 
##   205   206   207   208   209   210   211   212   213   214   215   216   217 
##  1412   357   792    96    24   128     1    25     2   145    28     2     1 
##   218   220   221   222   224   225   228   230   233   235   248   256   290 
##     5    21     7     1     3     3     4    13     1     1     1     1     1 
##   301   302   303   304   305   306   307   308   309   310   311   312   313 
##  5710  2801  1091   589   298   186    41    59     6   251     1    91     3 
##   314   315   316   317   318   319   320   321   322   323   324   325   328 
##    16   570    10     3     3     1    92     6     2     2     4    21     4 
##   329   330   331   333   335   340   343   345   350   360   363   365   370 
##     2   384     7     1     1     3     1     4     4    58     1     4     3 
##   375   390   398   399   401   402   403   404   405   406   407   408   409 
##    44    17     3    12   934  1795   672  1930   146   373    25    39    11 
##   410   411   412   415   416   417   418   420   421   422   424   425   426 
##    77     1    76    16     2     1     1    47     3     1     9    11     2 
##   428   430   433   436   440   444   448   450   452   460   465   467   472 
##     2    29     1     1     5     1     3    13     2     6     3     1     2 
##   475   488   490   498   499   777   888   999 
##    34     1     4     2    29  3454 25750   560

Var1	Freq
101	57317
102	39002
103	19240
104	10401
105	2417
106	1436
107	432
108	463
109	42
110	197
111	4
112	42
113	1
114	9
115	14
116	1
118	1
120	10
121	2
122	1
123	2
124	2
125	6
128	1
130	15
140	2
148	1
150	1
160	2
175	1
182	1
188	1
190	1
198	155
199	22
201	11683
202	8457
203	9408
204	3932
205	1412
206	357
207	792
208	96
209	24
210	128
211	1
212	25
213	2
214	145
215	28
216	2
217	1
218	5
220	21
221	7
222	1
224	3
225	3
228	4
230	13
233	1
235	1
248	1
256	1
290	1
301	5710
302	2801
303	1091
304	589
305	298
306	186
307	41
308	59
309	6
310	251
311	1
312	91
313	3
314	16
315	570
316	10
317	3
318	3
319	1
320	92
321	6
322	2
323	2
324	4
325	21
328	4
329	2
330	384
331	7
333	1
335	1
340	3
343	1
345	4
350	4
360	58
363	1
365	4
370	3
375	44
390	17
398	3
399	12
401	934
402	1795
403	672
404	1930
405	146
406	373
407	25
408	39
409	11
410	77
411	1
412	76
415	16
416	2
417	1
418	1
420	47
421	3
422	1
424	9
425	11
426	2
428	2
430	29
433	1
436	1
440	5
444	1
448	3
450	13
452	2
460	6
465	3
467	1
472	2
475	34
488	1
490	4
498	2
499	29
777	3454
888	25750
999	560

mymiss(total$BLDSUGAR,"Blood Sugar")

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

total$BldSugar=rep(0, nrow(total))
total$BldSugar[total$BLDSUGAR<500]=1
total$BldSugar[is.na(total$BLDSUGAR)]=NA
total$BldSugar=as.factor(total$BldSugar)
levels(total$BldSugar)=c("No", "Yes")
myprint(table(total$BldSugar)/sum(table(total$BldSugar)),"Blood Sugar")

## 
##        No       Yes 
## 0.1376147 0.8623853

Blood Sugar

Var1	Freq
No	0.1376147
Yes	0.8623853

###############################################################

Diabetes Doctor Visit

About how many times in the past 12 months have you seen a doctor, nurse, or other health professional for your diabetes?

0=None/Don’t Know/Refused/Blank, 1=1-76

###############################################################
myprint(table(total$DOCTDIAB))

## 
##     1     2     3     4     5     6     7     8     9    10    11    12    13 
## 29798 43458 28597 56263  4407  7791   924  1618   461  1313   191  8719    71 
##    14    15    16    17    18    19    20    21    22    23    24    25    26 
##   122   332    82    17    68     4   350     7    16     6   601    92    42 
##    28    29    30    31    32    33    34    35    36    37    40    41    42 
##    17     1   216     5    10    12    10    14    91     1    76     1     2 
##    43    44    45    46    47    48    50    52    54    55    56    58    60 
##    17     4    14     5     7    83    74   114     2     1     2     1    25 
##    64    70    72    75    76    77    88    99 
##     1     5     2     1   223  7129 21710  1044

Var1	Freq
1	29798
2	43458
3	28597
4	56263
5	4407
6	7791
7	924
8	1618
9	461
10	1313
11	191
12	8719
13	71
14	122
15	332
16	82
17	17
18	68
19	4
20	350
21	7
22	16
23	6
24	601
25	92
26	42
28	17
29	1
30	216
31	5
32	10
33	12
34	10
35	14
36	91
37	1
40	76
41	1
42	2
43	17
44	4
45	14
46	5
47	7
48	83
50	74
52	114
54	2
55	1
56	2
58	1
60	25
64	1
70	5
72	2
75	1
76	223
77	7129
88	21710
99	1044

mymiss(total$DOCTDIAB, "Doctor Visit")

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

total$DocDiab=rep(0,nrow(total))
total$DocDiab[na.omit(total$DOCTDIAB)<77]=1
total$DocDiab[is.na(total$DOCTDIAB)]=NA
total$DocDiab = as.factor(total$DocDiab)
levels(total$DocDiab)=c("No", "Yes" )
myprint(table(total$DocDiab)/sum(table(total$DocDiab)), "Diabetes Doctor")

## 
##        No       Yes 
## 0.1377722 0.8622278

Diabetes Doctor

Var1	Freq
No	0.1377722
Yes	0.8622278

###############################################################

Hemo Check

About how many times in the past 12 months has a doctor, nurse, or other health professional checked you for A-one-C?

1-76: number of times, 77: Don’t know / not sure, 88: None, 98: never heard of A1c, 99: Refused, NA: Blank.

###############################################################
myprint(table(total$CHKHEMO3))

## 
##     1     2     3     4     5     6     7     8     9    10    11    12    13 
## 33266 56216 30897 50973  2230  3613   349   546   212   374    41  2522    28 
##    14    15    16    17    18    19    20    21    22    23    24    25    26 
##    35    87    18     5    14     1    96     8     7    16   158    34     7 
##    27    28    30    31    32    33    34    35    36    38    40    41    42 
##     2     2    72     3     4     9     3     5    21     3    19     1     1 
##    43    44    45    46    47    48    49    50    52    54    55    56    57 
##    11    14     3     1     4    29     1    22    48     1     3     2     1 
##    60    70    72    74    75    76    77    88    98    99 
##    17     3     2     5     3   130 14639 12752  5947   734

Var1	Freq
1	33266
2	56216
3	30897
4	50973
5	2230
6	3613
7	349
8	546
9	212
10	374
11	41
12	2522
13	28
14	35
15	87
16	18
17	5
18	14
19	1
20	96
21	8
22	7
23	16
24	158
25	34
26	7
27	2
28	2
30	72
31	3
32	4
33	9
34	3
35	5
36	21
38	3
40	19
41	1
42	1
43	11
44	14
45	3
46	1
47	4
48	29
49	1
50	22
52	48
54	1
55	3
56	2
57	1
60	17
70	3
72	2
74	5
75	3
76	130
77	14639
88	12752
98	5947
99	734

mymiss(total$CHKHEMO3, "HbA1c Check")

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

total$HemChk=rep(0,nrow(total))
total$HemChk[total$CHKHEMO3<77]=1
total$HemChk[is.na(total$CHKHEMO3)]=NA
total$HemChk = as.factor(total$HemChk)
levels(total$HemChk)=c("No","Yes" )
myprint(table(total$HemChk)/sum(table(total$HemChk)), "A1C Check")

## 
##        No       Yes 
## 0.1575438 0.8424562

A1C Check

Var1	Freq
No	0.1575438
Yes	0.8424562

###############################################################

Feet Check

About how many times in the past 12 months has a health professional checked your feet for any sores or irritations?

1-76: number of times, 77: Don’t know / not sure, 88: None, 98: never heard of A1c, 99: Refused, NA: Blank.

###############################################################
myprint(table(total$FEETCHK))

## 
##     1     2     3     4     5     6     7     8     9    10    11    12    13 
## 44633 40158 19420 34219  2855  6339   575  1038   354   857    86  4720    32 
##    14    15    16    17    18    19    20    21    22    23    24    25    26 
##    59   187    48     8    28     2   222     6    11    18   408    68    40 
##    27    28    30    31    32    33    34    35    36    37    38    40    42 
##     5     9   164     5    10     5     1    11    60     1     1    49     6 
##    43    44    45    46    47    48    50    52    53    55    56    58    60 
##    10     7    10     1     4   102    75   304     1     7    12     1    31 
##    61    64    65    66    67    69    70    72    73    75    76    77    88 
##     2     2     1     2     1     1     4     6     1     1   392  6360 50089 
##    99 
##   689

Var1	Freq
1	44633
2	40158
3	19420
4	34219
5	2855
6	6339
7	575
8	1038
9	354
10	857
11	86
12	4720
13	32
14	59
15	187
16	48
17	8
18	28
19	2
20	222
21	6
22	11
23	18
24	408
25	68
26	40
27	5
28	9
30	164
31	5
32	10
33	5
34	1
35	11
36	60
37	1
38	1
40	49
42	6
43	10
44	7
45	10
46	1
47	4
48	102
50	75
52	304
53	1
55	7
56	12
58	1
60	31
61	2
64	2
65	1
66	2
67	1
69	1
70	4
72	6
73	1
75	1
76	392
77	6360
88	50089
99	689

mymiss(total$FEETCHK, "Feet Check")

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

total$FtChk=rep(0,nrow(total))
total$FtChk[total$FEETCHK<77]=1
total$FtChk[is.na(total$FEETCHK)]=NA
total$FtChk = as.factor(total$FtChk)
levels(total$FtChk)=c("No","Yes" )
myprint(table(total$FtChk)/sum(table(total$FtChk)), "Feet Check")

## 
##        No       Yes 
## 0.2659635 0.7340365

Feet Check

Var1	Freq
No	0.2659635
Yes	0.7340365

###############################################################

Diabetes Education

Have you ever taken a course or class in how to manage your diabetes yourself?

0=Not Yes, 1 = Yes…Original coding: 1=No, 2 = Yes, 7=Don’t know, 9= Refused, Blank=missing / not asked

###############################################################
myprint(table(total$DIABEDU))

## 
##      1      2      7      9 
## 116364  98988    740    154

Var1	Freq
1	116364
2	98988
7	740
9	154

mymiss(total$DIABEDU, "Education")

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

total$DiabEd=rep(0,nrow(total))
total$DiabEd[total$DIABEDU==1]=1
total$DiabEd[is.na(total$DIABEDU)]=NA
total$DiabEd=as.factor(total$DiabEd)
levels(total$DiabEd)=c("No", "Yes")
myprint(table(total$DiabEd)/sum(table(total$DiabEd)), "Diabetes Education")

## 
##        No       Yes 
## 0.4618906 0.5381094

Diabetes Education

Var1	Freq
No	0.4618906
Yes	0.5381094

###############################################################

Eye Examination

When was the last time you had an eye exam in which the pupils were dilated, making you temporarily sensitive to bright light? 0=Other than within 1 year, 1 = Within 1 year Original coding: 1: within month, 2: >month but within year, 3: > year but within 2 years, 4: 2+ years, 7: Don’t know / not sure, 8: Never, 9: Refused

###############################################################
myprint(table(total$EYEEXAM))

## 
##      1      2      3      4      7      8      9 
##  44121 110073  27257  25167   3364   5977    297

Var1	Freq
1	44121
2	110073
3	27257
4	25167
7	3364
8	5977
9	297

mymiss(total$EYEEXAM, "Eye Check")

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

total$EyeExam=rep(0,nrow(total))
total$EyeExam[total$EYEEXAM<=2]=1  #within one year
total$EyeExam[is.na(total$EYEEXAM)]=NA
total$EyeExam=as.factor(total$EyeExam)
levels(total$EyeExam)=c("No", "Yes")
myprint(table(total$EyeExam)/sum(table(total$EyeExam)), "Eye Exam")

## 
##        No       Yes 
## 0.2869839 0.7130161

Eye Exam

Var1	Freq
No	0.2869839
Yes	0.7130161

###############################################################

Missing plots for all DVs by Year

par(mfrow=c(2,3))
p1=mymiss(total$BldSugar, "Blood Sugar")

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

p2=mymiss(total$DocDiab, "Doctor Visit")

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

p3=mymiss(total$HemChk, "HbA1c Check")

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

p4=mymiss(total$EyeExam, "Eye Exam")

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

p5=mymiss(total$DiabEd, "Education")

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

p6=mymiss(total$FtChk, "Feet Check")

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

grid.arrange(p1,p2,p3, p4, p5, p6, nrow=3)

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

Clean Independent Variables

State

State FIPS Code

https://www.census.gov/library/reference/code-lists/ansi.html

Zero NA’s

total$X_STATE=recode(total$X_STATE, 
                     '1'='AL','2'='AK','4'='AZ','5'='AR','6'='CA','8'='CO','9'='CT','10'='DE','11'='DC','12'='FL','13'='GA','15'='HI','16'='ID','17'='IL','18'='IN','19'='IA','20'='KS','21'='KY','22'='LA','23'='ME','24'='MD','25'='MA','26'='MI','27'='MN','28'='MS','29'='MO','30'='MT','31'='NE','32'='NV','33'='NH','34'='NJ','35'='NM','36'='NY','37'='NC','38'='ND','39'='OH','40'='OK','41'='OR','42'='PA','44'='RI','45'='SC','46'='SD','47'='TN','48'='TX','49'='UT','50'='VT','51'='VA','53'='WA','54'='WV','55'='WI','56'='WY','66'='GU','72'='PR','78'='VI'
)

Medicaid Expansion

We will use linear splines for QB Log regression and dichotomous indicators for tables.

https://www.kff.org/medicaid/issue-brief/status-of-state-medicaid-expansion-decisions-interactive-map/

total$MedExp=rep(0, nrow(total)) #spline
total$MedExp2=rep(0,nrow(total)) #dichotomous backup

total$MedExp2[total$X_STATE=="AK"& total$Year>=2016]=1 #late adopter
total$MedExp[total$X_STATE=="AK"& total$Year==2016]=1 #late adopter
total$MedExp[total$X_STATE=="AK"& total$Year==2017]=2 #late adopter
total$MedExp[total$X_STATE=="AK"& total$Year==2018]=3 #late adopter
total$MedExp[total$X_STATE=="AK"& total$Year==2019]=4 #late adopter
#AL=NO

total$MedExp2[total$X_STATE=="AR"& total$Year>=2014]=1
total$MedExp[total$X_STATE=="AR"& total$Year==2014]=1
total$MedExp[total$X_STATE=="AR"& total$Year==2015]=2
total$MedExp[total$X_STATE=="AR"& total$Year==2016]=3
total$MedExp[total$X_STATE=="AR"& total$Year==2017]=4
total$MedExp[total$X_STATE=="AR"& total$Year==2018]=5
total$MedExp[total$X_STATE=="AR"& total$Year==2019]=6


total$MedExp2[total$X_STATE=="AZ"& total$Year>=2014]=1
total$MedExp[total$X_STATE=="AZ"& total$Year==2014]=1
total$MedExp[total$X_STATE=="AZ"& total$Year==2015]=2
total$MedExp[total$X_STATE=="AZ"& total$Year==2016]=3
total$MedExp[total$X_STATE=="AZ"& total$Year==2017]=4
total$MedExp[total$X_STATE=="AZ"& total$Year==2018]=5
total$MedExp[total$X_STATE=="AZ"& total$Year==2019]=6

total$MedExp2[total$X_STATE=="CA"& total$Year>=2014]=1
total$MedExp[total$X_STATE=="CA"& total$Year==2014]=1
total$MedExp[total$X_STATE=="CA"& total$Year==2015]=2
total$MedExp[total$X_STATE=="CA"& total$Year==2016]=3
total$MedExp[total$X_STATE=="CA"& total$Year==2017]=4
total$MedExp[total$X_STATE=="CA"& total$Year==2018]=5
total$MedExp[total$X_STATE=="CA"& total$Year==2019]=6


total$MedExp2[total$X_STATE=="CT"& total$Year>=2014]=1
total$MedExp[total$X_STATE=="CT"& total$Year==2014]=1
total$MedExp[total$X_STATE=="CT"& total$Year==2015]=2
total$MedExp[total$X_STATE=="CT"& total$Year==2016]=3
total$MedExp[total$X_STATE=="CT"& total$Year==2017]=4
total$MedExp[total$X_STATE=="CT"& total$Year==2018]=5
total$MedExp[total$X_STATE=="CT"& total$Year==2019]=6

total$MedExp2[total$X_STATE=="DC"& total$Year>=2014]=1
total$MedExp[total$X_STATE=="DC"& total$Year==2014]=1
total$MedExp[total$X_STATE=="DC"& total$Year==2015]=2
total$MedExp[total$X_STATE=="DC"& total$Year==2016]=3
total$MedExp[total$X_STATE=="DC"& total$Year==2017]=4
total$MedExp[total$X_STATE=="DC"& total$Year==2018]=5
total$MedExp[total$X_STATE=="DC"& total$Year==2019]=6

total$MedExp2[total$X_STATE=="DE"& total$Year>=2014]=1
total$MedExp[total$X_STATE=="DE"& total$Year==2014]=1
total$MedExp[total$X_STATE=="DE"& total$Year==2015]=2
total$MedExp[total$X_STATE=="DE"& total$Year==2016]=3
total$MedExp[total$X_STATE=="DE"& total$Year==2017]=4
total$MedExp[total$X_STATE=="DE"& total$Year==2018]=5
total$MedExp[total$X_STATE=="DE"& total$Year==2019]=6

#FL=NO
#GA=NO

total$MedExp2[total$X_STATE=="HI"& total$Year>=2014]=1
total$MedExp[total$X_STATE=="HI"& total$Year==2014]=1
total$MedExp[total$X_STATE=="HI"& total$Year==2015]=2
total$MedExp[total$X_STATE=="HI"& total$Year==2016]=3
total$MedExp[total$X_STATE=="HI"& total$Year==2017]=4
total$MedExp[total$X_STATE=="HI"& total$Year==2018]=5
total$MedExp[total$X_STATE=="HI"& total$Year==2019]=6


total$MedExp2[total$X_STATE=="IA"& total$Year>=2014]=1
total$MedExp[total$X_STATE=="IA"& total$Year==2014]=1
total$MedExp[total$X_STATE=="IA"& total$Year==2015]=2
total$MedExp[total$X_STATE=="IA"& total$Year==2016]=3
total$MedExp[total$X_STATE=="IA"& total$Year==2017]=4
total$MedExp[total$X_STATE=="IA"& total$Year==2018]=5
total$MedExp[total$X_STATE=="IA"& total$Year==2019]=6


total$MedExp2[total$X_STATE=="ID"& total$Year>=2020]=1
total$MedExp[total$X_STATE=="ID"& total$Year==2020]=1 #late adopter


total$MedExp2[total$X_STATE=="IL"& total$Year>=2014]=1
total$MedExp[total$X_STATE=="IL"& total$Year==2014]=1
total$MedExp[total$X_STATE=="IL"& total$Year==2015]=2
total$MedExp[total$X_STATE=="IL"& total$Year==2016]=3
total$MedExp[total$X_STATE=="IL"& total$Year==2017]=4
total$MedExp[total$X_STATE=="IL"& total$Year==2018]=5
total$MedExp[total$X_STATE=="IL"& total$Year==2019]=6

total$MedExp2[total$X_STATE=="IN"& total$Year>=2015]=1
total$MedExp[total$X_STATE=="IN"& total$Year==2015]=1 #late adopter
total$MedExp[total$X_STATE=="IN"& total$Year==2016]=2
total$MedExp[total$X_STATE=="IN"& total$Year==2017]=3
total$MedExp[total$X_STATE=="IN"& total$Year==2018]=4
total$MedExp[total$X_STATE=="IN"& total$Year==2019]=5

#KS=NO

total$MedExp2[total$X_STATE=="KY"& total$Year>=2014]=1
total$MedExp[total$X_STATE=="KY"& total$Year==2014]=1
total$MedExp[total$X_STATE=="KY"& total$Year==2015]=2
total$MedExp[total$X_STATE=="KY"& total$Year==2016]=3
total$MedExp[total$X_STATE=="KY"& total$Year==2017]=4
total$MedExp[total$X_STATE=="KY"& total$Year==2018]=5
total$MedExp[total$X_STATE=="KY"& total$Year==2019]=6

total$MedExp2[total$X_STATE=="LA"& total$Year>=2017]=1
total$MedExp[total$X_STATE=="LA"& total$Year==2017]=1 #late adopter
total$MedExp[total$X_STATE=="LA"& total$Year==2018]=2
total$MedExp[total$X_STATE=="LA"& total$Year==2019]=3

total$MedExp2[total$X_STATE=="MA"& total$Year>=2014]=1
total$MedExp[total$X_STATE=="MA"& total$Year==2014]=1
total$MedExp[total$X_STATE=="MA"& total$Year==2015]=2
total$MedExp[total$X_STATE=="MA"& total$Year==2016]=3
total$MedExp[total$X_STATE=="MA"& total$Year==2017]=4
total$MedExp[total$X_STATE=="MA"& total$Year==2018]=5
total$MedExp[total$X_STATE=="MA"& total$Year==2019]=6

total$MedExp2[total$X_STATE=="MD"& total$Year>=2014]=1
total$MedExp[total$X_STATE=="MD"& total$Year==2014]=1
total$MedExp[total$X_STATE=="MD"& total$Year==2015]=2
total$MedExp[total$X_STATE=="MD"& total$Year==2016]=3
total$MedExp[total$X_STATE=="MD"& total$Year==2017]=4
total$MedExp[total$X_STATE=="MD"& total$Year==2018]=5
total$MedExp[total$X_STATE=="MD"& total$Year==2019]=6


total$MedExp2[total$X_STATE=="ME"& total$Year>=2019]=1
total$MedExp[total$X_STATE=="ME"& total$Year==2019]=1 #late adopter

total$MedExp2[total$X_STATE=="MI"& total$Year>=2014]=1
total$MedExp[total$X_STATE=="MI"& total$Year==2014]=1 
total$MedExp[total$X_STATE=="MI"& total$Year==2015]=2
total$MedExp[total$X_STATE=="MI"& total$Year==2016]=3
total$MedExp[total$X_STATE=="MI"& total$Year==2017]=4
total$MedExp[total$X_STATE=="MI"& total$Year==2018]=5
total$MedExp[total$X_STATE=="MI"& total$Year==2019]=6

total$MedExp2[total$X_STATE=="MN"& total$Year>=2014]=1
total$MedExp[total$X_STATE=="MN"& total$Year==2014]=1
total$MedExp[total$X_STATE=="MN"& total$Year==2015]=2
total$MedExp[total$X_STATE=="MN"& total$Year==2016]=3
total$MedExp[total$X_STATE=="MN"& total$Year==2017]=4
total$MedExp[total$X_STATE=="MN"& total$Year==2018]=5
total$MedExp[total$X_STATE=="MN"& total$Year==2019]=6

#MO=Adopted but not implemented
#MS=NO

total$MedExp2[total$X_STATE=="MT"& total$Year>=2016]=1
total$MedExp[total$X_STATE=="MT"& total$Year==2016]=1 #late adopter
total$MedExp[total$X_STATE=="MT"& total$Year==2017]=2
total$MedExp[total$X_STATE=="MT"& total$Year==2018]=3
total$MedExp[total$X_STATE=="MT"& total$Year==2019]=4
total$MedExp[total$X_STATE=="MT"& total$Year==2020]=5

#NC=NO

total$MedExp2[total$X_STATE=="ND"& total$Year>=2021]=1
total$MedExp[total$X_STATE=="ND"& total$Year==2021]=1

total$MedExp2[total$X_STATE=="NE"& total$Year>=2020]=1
total$MedExp[total$X_STATE=="NE"& total$Year==2020]=1 #late adopter

total$MedExp2[total$X_STATE=="NH"& total$Year>=2015]=1
total$MedExp[total$X_STATE=="NH"& total$Year==2015]=1 #late adopter
total$MedExp[total$X_STATE=="NH"& total$Year==2016]=2
total$MedExp[total$X_STATE=="NH"& total$Year==2017]=3
total$MedExp[total$X_STATE=="NH"& total$Year==2018]=4
total$MedExp[total$X_STATE=="NH"& total$Year==2019]=5

total$MedExp2[total$X_STATE=="NJ"& total$Year>=2014]=1
total$MedExp[total$X_STATE=="NJ"& total$Year==2014]=1
total$MedExp[total$X_STATE=="NJ"& total$Year==2015]=2
total$MedExp[total$X_STATE=="NJ"& total$Year==2016]=3
total$MedExp[total$X_STATE=="NJ"& total$Year==2017]=4
total$MedExp[total$X_STATE=="NJ"& total$Year==2018]=5
total$MedExp[total$X_STATE=="NJ"& total$Year==2019]=6

total$MedExp2[total$X_STATE=="NM"& total$Year>=2014]=1
total$MedExp[total$X_STATE=="NM"& total$Year==2014]=1
total$MedExp[total$X_STATE=="NM"& total$Year==2015]=2
total$MedExp[total$X_STATE=="NM"& total$Year==2016]=3
total$MedExp[total$X_STATE=="NM"& total$Year==2017]=4
total$MedExp[total$X_STATE=="NM"& total$Year==2018]=5
total$MedExp[total$X_STATE=="NM"& total$Year==2019]=6

total$MedExp2[total$X_STATE=="NV"& total$Year>=2014]=1
total$MedExp[total$X_STATE=="NV"& total$Year==2014]=1
total$MedExp[total$X_STATE=="NV"& total$Year==2015]=2
total$MedExp[total$X_STATE=="NV"& total$Year==2016]=3
total$MedExp[total$X_STATE=="NV"& total$Year==2017]=4
total$MedExp[total$X_STATE=="NV"& total$Year==2018]=5
total$MedExp[total$X_STATE=="NV"& total$Year==2019]=6

total$MedExp2[total$X_STATE=="NY"& total$Year>=2014]=1
total$MedExp[total$X_STATE=="NY"& total$Year==2014]=1
total$MedExp[total$X_STATE=="NY"& total$Year==2015]=2
total$MedExp[total$X_STATE=="NY"& total$Year==2016]=3
total$MedExp[total$X_STATE=="NY"& total$Year==2017]=4
total$MedExp[total$X_STATE=="NY"& total$Year==2018]=5
total$MedExp[total$X_STATE=="NY"& total$Year==2019]=6

total$MedExp2[total$X_STATE=="OH"& total$Year>=2014]=1
total$MedExp[total$X_STATE=="OH"& total$Year==2014]=1
total$MedExp[total$X_STATE=="OH"& total$Year==2015]=2
total$MedExp[total$X_STATE=="OH"& total$Year==2016]=3
total$MedExp[total$X_STATE=="OH"& total$Year==2017]=4
total$MedExp[total$X_STATE=="OH"& total$Year==2018]=5
total$MedExp[total$X_STATE=="OH"& total$Year==2019]=6


#OK=Adopted but not implemented

total$MedExp2[total$X_STATE=="OR"& total$Year>=2014]=1
total$MedExp[total$X_STATE=="OR"& total$Year==2014]=1
total$MedExp[total$X_STATE=="OR"& total$Year==2015]=2
total$MedExp[total$X_STATE=="OR"& total$Year==2016]=3
total$MedExp[total$X_STATE=="OR"& total$Year==2017]=4
total$MedExp[total$X_STATE=="OR"& total$Year==2018]=5
total$MedExp[total$X_STATE=="OR"& total$Year==2019]=6

total$MedExp2[total$X_STATE=="PA"& total$Year>=2014]=1
total$MedExp[total$X_STATE=="PA"& total$Year==2015]=1  #late adopter
total$MedExp[total$X_STATE=="PA"& total$Year==2016]=2
total$MedExp[total$X_STATE=="PA"& total$Year==2017]=3
total$MedExp[total$X_STATE=="PA"& total$Year==2018]=4
total$MedExp[total$X_STATE=="PA"& total$Year==2019]=5

total$MedExp2[total$X_STATE=="RI"& total$Year>=2014]=1
total$MedExp[total$X_STATE=="RI"& total$Year==2014]=1
total$MedExp[total$X_STATE=="RI"& total$Year==2015]=2
total$MedExp[total$X_STATE=="RI"& total$Year==2016]=3
total$MedExp[total$X_STATE=="RI"& total$Year==2017]=4
total$MedExp[total$X_STATE=="RI"& total$Year==2018]=5
total$MedExp[total$X_STATE=="RI"& total$Year==2019]=6

#SC=NO
#SD=NO
#TN=NO
#TX=NO

total$MedExp2[total$X_STATE=="UT"& total$Year>=2020]=1
total$MedExp[total$X_STATE=="UT"& total$Year==2020]=1  #late adopter

total$MedExp2[total$X_STATE=="VA"& total$Year>=2019]=1
total$MedExp[total$X_STATE=="VA"& total$Year==2019]=1  #late adopter

total$MedExp2[total$X_STATE=="VT"& total$Year>=2014]=1
total$MedExp[total$X_STATE=="VT"& total$Year==2014]=1
total$MedExp[total$X_STATE=="VT"& total$Year==2015]=2
total$MedExp[total$X_STATE=="VT"& total$Year==2016]=3
total$MedExp[total$X_STATE=="VT"& total$Year==2017]=4
total$MedExp[total$X_STATE=="VT"& total$Year==2018]=5
total$MedExp[total$X_STATE=="VT"& total$Year==2019]=6

total$MedExp2[total$X_STATE=="WA"& total$Year>=2014]=1
total$MedExp[total$X_STATE=="WA"& total$Year==2014]=1
total$MedExp[total$X_STATE=="WA"& total$Year==2015]=2
total$MedExp[total$X_STATE=="WA"& total$Year==2016]=3
total$MedExp[total$X_STATE=="WA"& total$Year==2017]=4
total$MedExp[total$X_STATE=="WA"& total$Year==2018]=5
total$MedExp[total$X_STATE=="WA"& total$Year==2019]=6

#WI=NO

total$MedExp2[total$X_STATE=="WV"& total$Year>=2014]=1
total$MedExp[total$X_STATE=="WV"& total$Year==2014]=1
total$MedExp[total$X_STATE=="WV"& total$Year==2015]=2
total$MedExp[total$X_STATE=="WV"& total$Year==2016]=3
total$MedExp[total$X_STATE=="WV"& total$Year==2017]=4
total$MedExp[total$X_STATE=="WV"& total$Year==2018]=5
total$MedExp[total$X_STATE=="WV"& total$Year==2019]=6

#WY=NO
total$MedExp2=as.factor(total$MedExp2)
levels(total$MedExp2)=c("No","Yes")
myprint(table(total$MedExp2)/sum(table(total$MedExp2)), "Medicaid Expansion")

## 
##        No       Yes 
## 0.6465515 0.3534485

Medicaid Expansion

Var1	Freq
No	0.6465515
Yes	0.3534485

plot(as.factor(total$MedExp2)~as.factor(total$Year), ylab='', xlab="Year", main="Medicaid Expansion")

## Gender

What is your sex? or What was your sex at birth? Was it…

0=Female, 1=Male, 7=DK, 9=refused

########################Demographics############################
mymiss(total$SEX)

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

total$Male=as.numeric(total$SEX)  #No missing
total$Male[total$SEX==2]=0 #females are 2 recoded to 0
total$Male[total$SEX==1]=1 #males are 1
total$Male[total$SEX>2]=0 #only 207 total in cat 7 or 9 and NO NAs
total$Male=as.factor(total$Male)
levels(total$Male)=c("Female", "Male")
myprint(table(total$Male)/sum(table(total$Male)), "Male?")

## 
##    Female      Male 
## 0.5569844 0.4430156

Male?

Var1	Freq
Female	0.5569844
Male	0.4430156

plot(total$Male~as.factor(total$Year))

###############################################################

Marital Status

Are you: (marital status) 1=Married 2=Divorced 3=Widowed 4=Separated 5=Never Married 6=Member of Unmarried Couple 9=Don’t Know Almost 0% NA

###############################################################
mymiss(total$MARITAL)

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

total$Married=total$MARITAL
total$Married[is.na(total$MARITAL)]=9 #refused, don't know
total$Married=as.factor(total$Married)
levels(total$Married)=c("Married", "Divorced", "Widowed", "Separated",
                        "Never Married", "Unmarried Couple", 
                        "Unknown")
myprint(table(total$Married)/sum(table(total$Married)), "Married?")

## 
##          Married         Divorced          Widowed        Separated 
##      0.481683630      0.170445272      0.216921629      0.025771491 
##    Never Married Unmarried Couple          Unknown 
##      0.087434025      0.012632316      0.005111636

Married?

Var1	Freq
Married	0.4816836
Divorced	0.1704453
Widowed	0.2169216
Separated	0.0257715
Never Married	0.0874340
Unmarried Couple	0.0126323
Unknown	0.0051116

###############################################################

Age Group

45-54 (dichotomous) 55-64 (dichotomous) 65+ (dichotomous)

mymiss(total$X_AGE_G) #no missing

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

total$Age=total$X_AGE_G
total$Age=as.factor(total$Age)
levels(total$Age)=c("45-54", "55-64","65+")
myprint(table(total$Age)/sum(table(total$Age)), "Age")

## 
##     45-54     55-64       65+ 
## 0.1377715 0.2848543 0.5773742

Age

Var1	Freq
45-54	0.1377715
55-64	0.2848543
65+	0.5773742

plot(total$Age~as.factor(total$Year))

Race & Ethnicity

Race groups used for internet prevalence tables

1 White - Non-Hispanic 2 Black - Non-Hispanic 3 Hispanic 4 Other race only, Non-Hispanic 5 Multiracial, Non-Hispanic 6 Don’t know / refused / not sure

mymiss(total$X_RACE_G1)

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

total$Race=as.numeric(total$X_RACE_G1)
total$Race[is.na(total$X_RACE_G1)]=6
total$Race=as.factor(total$Race)

levels(total$Race)=c("White Non-Hispanic", "Black Non-Hispanic", "Hispanic", "Other race only Non-Hispanic", "Multi-racial Non-Hispanic","Unknown")
myprint(table(total$Race)/sum(table(total$Race)), "Race")

## 
##           White Non-Hispanic           Black Non-Hispanic 
##                   0.71477384                   0.12050114 
##                     Hispanic Other race only Non-Hispanic 
##                   0.07968633                   0.04638242 
##    Multi-racial Non-Hispanic                      Unknown 
##                   0.01967158                   0.01898468

Race

Var1	Freq
White Non-Hispanic	0.7147738
Black Non-Hispanic	0.1205011
Hispanic	0.0796863
Other race only Non-Hispanic	0.0463824
Multi-racial Non-Hispanic	0.0196716
Unknown	0.0189847

Education

What is the highest grade or year of school you completed?

1=Never attended / Kindergarten only 2=Grades 1 through 8 3=Grades 9 through 11 4=Grades 12 or GED 5=College 1 to 3 6=College 4 or more 9=Unknown

###########################Education###########################
mymiss(total$EDUCA)

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

total$Education=as.numeric(total$EDUCA)
total$Education[total$EDUCA<=2]=1
total$Education[is.na(total$EDUCA)]=9 # NA=unknown, so few
total$Education=as.factor(total$Education)
levels(total$Education)=c("<=8th", "Grades 9-11", 
                          "Grade 12 / GED", "College 1-3", "College 4+", "Unknown")
myprint(table(total$Education)/sum(table(total$Education)), "College, 4 Years")

## 
##          <=8th    Grades 9-11 Grade 12 / GED    College 1-3     College 4+ 
##    0.051901110    0.079882033    0.332909973    0.276750471    0.254311720 
##        Unknown 
##    0.004244693

College, 4 Years

Var1	Freq
<=8th	0.0519011
Grades 9-11	0.0798820
Grade 12 / GED	0.3329100
College 1-3	0.2767505
College 4+	0.2543117
Unknown	0.0042447

###############################################################

Income

Is your annual household income from all sources: (If respondent refuses at any income level, code ´Refused.´)

1: <$10K 2: <$15K 3: <$20K 4: <$25K 5: <$35K 6: <$50K 7: <$75K 8: $75K+ 77: Don’t Know / Not Sure 99: Refused

###############################################################
mymiss(total$INCOME2)

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

total$Income=as.numeric(total$INCOME2)
total$Income[is.na(total$INCOME2)]=9  #Treat as unknown
total$Income[total$INCOME2>8]=9
total$Income=as.factor(total$Income)
levels(total$Income)=c("LT10K", "LT15K", "LT20K", "LT25K",
                       "LT35K", "LT50K", "LT75K", "GTE75K", 
                       "Unknown")
myprint(table(total$Income)/sum(table(total$Income)), "Income")

## 
##      LT10K      LT15K      LT20K      LT25K      LT35K      LT50K      LT75K 
## 0.06641604 0.08098381 0.09371985 0.10293723 0.10822304 0.12073794 0.11071428 
##     GTE75K    Unknown 
## 0.14401819 0.17224961

Income

Var1	Freq
LT10K	0.0664160
LT15K	0.0809838
LT20K	0.0937199
LT25K	0.1029372
LT35K	0.1082230
LT50K	0.1207379
LT75K	0.1107143
GTE75K	0.1440182
Unknown	0.1722496

Employment

Are you currently…?

1=Employed for Wages 2=Self-Employed 3=Out of Work 1 year or More 4=Our of Work <1 year 5=Homemaker 6=Student 7=Retired 8=Unable to Work 9=Refused 0% NA

###########################Employoment###########################
mymiss(total$EMPLOY1)

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

total$Employ=as.numeric(total$EMPLOY1)
total$Employ[is.na(total$EMPLOY1)]=9 #Treat as unknown
total$Employ=as.factor(total$Employ)
levels(total$Employ)=c("Employed",  "Self-Employed",
            "Out of Work GTE 1 Year", "Out of Work LT 1 Year",
            "Homemaker", "Student",  "Retired", 
            "Unable to Work", "Unknown")
myprint(table(total$Employ)/sum(table(total$Employ)), "Employment")

## 
##               Employed          Self-Employed Out of Work GTE 1 Year 
##             0.20568464             0.04806543             0.02396715 
##  Out of Work LT 1 Year              Homemaker                Student 
##             0.01265580             0.04884626             0.00159494 
##                Retired         Unable to Work                Unknown 
##             0.48697531             0.16496181             0.00724866

Employment

Var1	Freq
Employed	0.2056846
Self-Employed	0.0480654
Out of Work GTE 1 Year	0.0239672
Out of Work LT 1 Year	0.0126558
Homemaker	0.0488463
Student	0.0015949
Retired	0.4869753
Unable to Work	0.1649618
Unknown	0.0072487

###############################################################

Health Plan

Do you have any kind of health care coverage, including health insurance, prepaid plans such as HMOs, or government plans such as Medicare, or Indian Health Service?

###############################################################
mymiss(total$HLTHPLN1)

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

total$HlthPln=as.numeric(total$HLTHPLN1)
total$HlthPln[is.na(total$HLTHPLN1)]=3 #only 3 missing, all years:  modal
total$HlthPln[total$HlthPln>=7]=3
total$HlthPln=as.factor(total$HlthPln)
levels(total$HlthPln)=c("Yes", "No", "Unknown")
plot(total$HlthPln~total$Year, ylab='', xlab="Year", main="Health Plan")

###############################################################

Personal Doctor

Do you have one person you think of as your personal doctor or health care provider?

0=NO, 1=YES

###############################################################
mymiss(total$PERSDOC2)

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

total$PersDoc=as.numeric(total$PERSDOC2)
total$PersDoc[total$PERSDOC2==3]=0 #no visits coded 3!
total$PersDoc[total$PERSDOC2<=2]=1 #one or more visits
total$PersDoc[total$PERSDOC2==7]=2 #Unknown
total$PersDoc[total$PERSDOC2==9]=2 #Unknown
total$PersDoc[is.na(total$PERSDOC2)]=2

total$PersDoc=as.factor(total$PersDoc)
levels(total$PersDoc)=c("No", "Yes", "Unknown")
myprint(table(total$PersDoc)/sum(table(total$PersDoc)), "Personal Doctor")

## 
##          No         Yes     Unknown 
## 0.048112393 0.948314158 0.003573448

Personal Doctor

Var1	Freq
No	0.0481124
Yes	0.9483142
Unknown	0.0035734

plot(total$PersDoc~as.factor(total$Year))

###############################################################

Check Up Last Year

About how long has it been since you last visited a doctor for a routine checkup?

0=greater than 1 year, 1 = within 1 year

###############################################################
mymiss(total$CHECKUP1)

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

total$Checkup=as.numeric(total$CHECKUP1)
total$Checkup[total$CHECKUP1>1 & total$CHECKUP1<5]=0
total$Checkup[total$CHECKUP1==1]=1 
total$Checkup[total$CHECKUP1>=5]=2
total$Checkup[is.na(total$CHECKUP1)]=1 #117 NA's in 9 years, modal impute

total$Checkup=as.factor(total$Checkup)
levels(total$Checkup)=c("No", "Yes", "Unknown")
myprint(table(total$Checkup)/sum(table(total$Checkup)), "Checkup")

## 
##         No        Yes    Unknown 
## 0.08603283 0.89816455 0.01580263

Checkup

Var1	Freq
No	0.0860328
Yes	0.8981645
Unknown	0.0158026

plot(total$Checkup~as.factor(total$Year), ylab='', xlab='Year', main="Checkup")

###############################################################

Cost

Was there a time in the past 12 months when you needed to see a doctor but could not because of cost?

0=NO, 1=YES

Almost no NA’s

###############################################################
mymiss(total$MEDCOST)

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

total$MedCost=as.numeric(total$MEDCOST)
total$MedCost[total$MEDCOST==2]=0
total$MedCost[total$MEDCOST==1]=1
total$MedCost[total$MEDCOST>6]=2
total$MedCost[is.na(total$MEDCOST)]=2
total$MedCost[total$MEDCOST]

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##  [ reached getOption("max.print") -- omitted 410992 entries ]

total$MedCost=as.factor(total$MedCost)
levels(total$MedCost)=c("No", "Yes", "Unknown")
myprint(table(total$MedCost)/sum(table(total$MedCost)), "Cost of Care")

## 
##          No         Yes     Unknown 
## 0.885770982 0.110530322 0.003698695

Cost of Care

Var1	Freq
No	0.8857710
Yes	0.1105303
Unknown	0.0036987

plot(total$MedCost~as.factor(total$Year))

###############################################################

Diabetes of the Eye

Has a doctor ever told you that diabetes has affected your eyes or that you had retinopathy?

mymiss(total$DIABEYE)

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

total$DiabEye=as.numeric(total$DIABEYE)
total$DiabEye[total$DIABEYE==1]=1  # dichotomous
total$DiabEye[total$DIABEYE==2]=0  # dichotomous
total$DiabEye[total$DIABEYE==7]=2  # dichotomous
total$DiabEye[total$DIABEYE==9]=2  # dichotomous
total$DiabEye[is.na(total$DIABEYE)]=2  # Retain NA's
total$DiabEye=as.factor(total$DiabEye)
levels(total$DiabEye)=c("No", "Yes", "Unknown")
myprint(table(total$DiabEye)/sum(table(total$DiabEye)), "Diabetes of the Eye?")

## 
##         No        Yes    Unknown 
## 0.33820557 0.07904249 0.58275195

Diabetes of the Eye?

Var1	Freq
No	0.3382056
Yes	0.0790425
Unknown	0.5827519

plot(total$DiabEye~as.factor(total$Year))

###############################################################

General Health

Would you say that in general your health is:

1=Excellent 2=Very Good 3=Good 4=Fair 5=Poor 7=Don’t Know 9=Refused 0% NA’s

###############################################################
mymiss(total$GENHLTH) # 0 missing

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

total$GenHlth=as.numeric(total$GENHLTH)
total$GenHlth[total$GENHLTH>5]= 6  #unknown
total$GenHlth=as.factor(total$GenHlth)
levels(total$GenHlth)=c("Excellent", "Very Good", "Good", "Fair", "Poor", "Unknown")
myprint(table(total$GenHlth)/sum(table(total$GenHlth)), "General Health")

## 
##  Excellent  Very Good       Good       Fair       Poor    Unknown 
## 0.03153536 0.16154972 0.36341985 0.28895147 0.15013640 0.00440720

General Health

Var1	Freq
Excellent	0.0315354
Very Good	0.1615497
Good	0.3634198
Fair	0.2889515
Poor	0.1501364
Unknown	0.0044072

Weights and Stratum

total$Stratum=total$X_STSTR
total$Weights=total$X_LLCPWT
total$X_STSTR=total$X_LLCPWT=NULL

Tables Based on Medicaid Expansion

t1=table(total$MedExp2, total$BldSugar)/sum(table(total$MedExp2, total$BldSugar))
t2=table(total$MedExp2, total$DocDiab)/sum(table(total$MedExp2, total$DocDiab))
t3=table(total$MedExp2, total$HemChk)/sum(table(total$MedExp2, total$HemChk))
t4=table(total$MedExp2, total$FtChk)/sum(table(total$MedExp2, total$FtChk))
t5=table(total$MedExp2, total$DiabEd)/sum(table(total$MedExp2, total$DiabEd))
t6=table(total$MedExp2, total$EyeExam)/sum(table(total$MedExp2, total$EyeExam))

t1a=table(total$MedExp2, total$BldSugar)
t2a=table(total$MedExp2, total$DocDiab)
t3a=table(total$MedExp2, total$HemChk)
t4a=table(total$MedExp2, total$FtChk)
t5a=table(total$MedExp2, total$DiabEd)
t6a=table(total$MedExp2, total$EyeExam)


myprint(t1, "Blood Sugar vs. Medicaid Expansion")

##      
##               No        Yes
##   No  0.08617796 0.57516240
##   Yes 0.05143676 0.28722288

Blood Sugar vs. Medicaid Expansion

	No	Yes
No	0.0861780	0.5751624
Yes	0.0514368	0.2872229

t1[2,2]/t1[2,1]/(t1[1,2]/t1[1,1])

## [1] 0.8366641

myprint(t1a)

##      
##           No    Yes
##   No   18639 124399
##   Yes  11125  62122

	No	Yes
No	18639	124399
Yes	11125	62122

myprint(t2, "Diabetes Dr. vs. Medicaid Expansion")

##      
##               No        Yes
##   No  0.09152448 0.56982476
##   Yes 0.04624775 0.29240301

Diabetes Dr. vs. Medicaid Expansion

	No	Yes
No	0.0915245	0.5698248
Yes	0.0462477	0.2924030

 t2[2,2]/t2[2,1]/(t2[1,2]/t2[1,1])

## [1] 1.015517

 myprint(t2a)

##      
##           No    Yes
##   No   19794 123236
##   Yes  10002  63238

	No	Yes
No	19794	123236
Yes	10002	63238

myprint(t3, "Hemo Checks vs. Medicaid Expansion")

##      
##               No        Yes
##   No  0.11438942 0.54696907
##   Yes 0.04315439 0.29548712

Hemo Checks vs. Medicaid Expansion

	No	Yes
No	0.1143894	0.5469691
Yes	0.0431544	0.2954871

t3[2,2]/t3[2,1]/(t3[1,2]/t3[1,1])

## [1] 1.431979

myprint(t3a)

##      
##           No    Yes
##   No   24739 118293
##   Yes   9333  63905

	No	Yes
No	24739	118293
Yes	9333	63905

myprint(t4, "Feet Checks vs. Medicaid Expansion")

##      
##               No        Yes
##   No  0.18448663 0.47701481
##   Yes 0.08147686 0.25702170

Feet Checks vs. Medicaid Expansion

	No	Yes
No	0.1844866	0.4770148
Yes	0.0814769	0.2570217

t4[2,2]/t4[2,1]/(t4[1,2]/t4[1,1])

## [1] 1.220024

myprint(t4a)

##      
##           No    Yes
##   No   39634 102479
##   Yes  17504  55217

	No	Yes
No	39634	102479
Yes	17504	55217

myprint(t5, "Diabetes Education vs. Medicaid Expansion")

##      
##              No       Yes
##   No  0.3104289 0.3509475
##   Yes 0.1514618 0.1871618

Diabetes Education vs. Medicaid Expansion

	No	Yes
No	0.3104289	0.3509475
Yes	0.1514618	0.1871618

t5[2,2]/t5[2,1]/(t5[1,2]/t5[1,1])

## [1] 1.093035

myprint(t5a)

##      
##          No   Yes
##   No  67129 75891
##   Yes 32753 40473

	No	Yes
No	67129	75891
Yes	32753	40473

myprint(t6, "Eye Exam vs. Medicaid Expansion")

##      
##               No        Yes
##   No  0.19601768 0.46534663
##   Yes 0.09096626 0.24766943

Eye Exam vs. Medicaid Expansion

	No	Yes
No	0.1960177	0.4653466
Yes	0.0909663	0.2476694

t6[2,2]/t6[2,1]/(t6[1,2]/t6[1,1])

## [1] 1.146861

myprint(t6a)

##      
##           No    Yes
##   No   42390 100634
##   Yes  19672  53560

	No	Yes
No	42390	100634
Yes	19672	53560

Count Remaining NA’s

for (i in 1:ncol(total)){
  if(sum(is.na(total[,i]))>0){print(colnames(total)[i])}
}

## [1] "X_RACE_G1"
## [1] "MARITAL"
## [1] "INCOME2"
## [1] "EDUCA"
## [1] "EMPLOY1"
## [1] "GENHLTH"
## [1] "HLTHPLN1"
## [1] "PERSDOC2"
## [1] "CHECKUP1"
## [1] "MEDCOST"
## [1] "DIABAGE2"
## [1] "DIABEDU"
## [1] "INSULIN"
## [1] "BLDSUGAR"
## [1] "FEETCHK"
## [1] "DOCTDIAB"
## [1] "CHKHEMO3"
## [1] "DIABEYE"
## [1] "EYEEXAM"
## [1] "BldSugar"
## [1] "DocDiab"
## [1] "HemChk"
## [1] "FtChk"
## [1] "DiabEd"
## [1] "EyeExam"
## [1] "GenHlth"

Make Survey Data

Apply the stratum and weights for population estimation.

makesurvey=function(surveydata){
  options(survey.lonely.psu = "adjust")
  
  mysurvey <- svydesign(
    id=~1,
    strata = ~Stratum,
    weights = ~Weights,
    data = surveydata)
  return(mysurvey)
}

svy2011=makesurvey(total[total$Year==2011,])
svy2012=makesurvey(total[total$Year==2012,])
svy2013=makesurvey(total[total$Year==2013,])
svy2014=makesurvey(total[total$Year==2014,])
svy2015=makesurvey(total[total$Year==2015,])
svy2016=makesurvey(total[total$Year==2016,])
svy2017=makesurvey(total[total$Year==2017,])
svy2018=makesurvey(total[total$Year==2018,])
svy2019=makesurvey(total[total$Year==2019,])
svytot=makesurvey(total)

Descriptives

Evaluate descriptives based on the applied weights / stratum.

All Years

options(scipen=999)
mymat=function(f){
  t1=as.data.frame(svymean(as.formula(f),svytot, na.rm=TRUE))
  t2=as.data.frame(svytotal(as.formula(f), svytot, na.rm=TRUE))
  t3=cbind(t1,t2)
  colnames(t3)=c("Mean", "SE", "Total", "SE")
  return(t3)
}

for (i in 1:length(colnames(svytot)))
{if (i==1){temp2=mymat(paste0("~", colnames(svytot)[i]))} else
  {temp=mymat(paste0("~", colnames(svytot)[i]))
  temp2=rbind(temp2, temp)}
  }

temp2%>%kbl()%>%kable_classic(html_font='Cambria')

	Mean	SE	Total	SE
IMONTH	6.5956617	0.0103915	1366423403.937	3641271.8834
X_AGE_G	5.2772705	0.0024873	1093292259.384	2290258.2731
X_RACE_G1	1.7001297	0.0036618	345508991.685	1276738.9786
SEX	1.4974825	0.0015921	310233488.287	714053.6111
MARITAL	2.0336490	0.0045279	421124942.350	1242041.6670
INCOME2	18.4703692	0.0961030	3799168585.130	21574928.1735
EDUCA	4.3292594	0.0041773	896342716.935	1816725.4136
EMPLOY1	5.2116349	0.0088152	1077625461.885	2807322.2965
GENHLTH	3.4346608	0.0033957	711540083.094	1706839.3527
HLTHPLN1	1.0937966	0.0013617	226598805.272	574329.3706
PERSDOC2	1.2523073	0.0022991	259357159.522	751789.6458
CHECKUP1	1.2443398	0.0027981	257708104.952	800587.3927
MEDCOST	1.8846528	0.0015759	390314279.413	879257.9028
DIABAGE2	54.0276625	0.0582247	9180316885.948	23934832.0719
DIABEDU	1.4981773	0.0027788	119707878.257	510174.8804
INSULIN	1.6938588	0.0024932	135351384.315	528442.1277
BLDSUGAR	247.7749615	1.1576207	19800119007.163	116632032.3078
FEETCHK	26.2764652	0.1713064	2084043882.511	15994257.3625
DOCTDIAB	15.0841108	0.1198883	1205388824.955	10344852.5585
CHKHEMO3	16.8649337	0.1409137	1347706628.942	12431306.8578
DIABEYE	1.8717316	0.0032232	149554613.043	597858.4638
EYEEXAM	2.4782598	0.0076299	198027359.476	1003843.8191
X_STATEAK	0.0014977	0.0000301	310277.775	6199.7246
X_STATEAL	0.0188584	0.0001422	3906890.399	28802.7857
X_STATEAR	0.0105519	0.0001000	2186036.421	20387.3151
X_STATEAZ	0.0202348	0.0002672	4192042.741	55724.2756
X_STATECA	0.1112468	0.0009977	23047002.286	227734.2415
X_STATECO	0.0108103	0.0000799	2239565.371	16021.5292
X_STATECT	0.0101037	0.0000987	2093174.125	20168.6622
X_STATEDC	0.0016791	0.0000244	347857.376	5001.8244
X_STATEDE	0.0031404	0.0000344	650595.354	7015.9516
X_STATEFL	0.0713852	0.0006058	14788872.213	131242.7632
X_STATEGA	0.0329109	0.0002727	6818152.793	56503.3313
X_STATEGU	0.0004296	0.0000100	88997.324	2067.3656
X_STATEHI	0.0039015	0.0000450	808282.066	9200.5442
X_STATEIA	0.0086338	0.0000589	1788663.018	11690.2283
X_STATEID	0.0041338	0.0000431	856406.080	8779.0839
X_STATEIL	0.0379417	0.0003833	7860390.550	80755.1149
X_STATEIN	0.0214660	0.0001473	4447104.754	29656.2800
X_STATEKS	0.0082558	0.0000489	1710346.247	9542.6665
X_STATEKY	0.0157703	0.0001527	3267139.062	31348.3969
X_STATELA	0.0164596	0.0001677	3409943.664	34560.1719
X_STATEMA	0.0179179	0.0001934	3712052.346	40014.9020
X_STATEMD	0.0183628	0.0001619	3804218.355	33161.6271
X_STATEME	0.0041784	0.0000343	865637.827	6902.6430
X_STATEMI	0.0316874	0.0002225	6564676.895	45401.5223
X_STATEMN	0.0126516	0.0001019	2621027.646	20614.0538
X_STATEMO	0.0190046	0.0001745	3937179.440	35849.6558
X_STATEMS	0.0112971	0.0000918	2340420.250	18569.6375
X_STATEMT	0.0025239	0.0000240	522878.837	4859.2750
X_STATENC	0.0330159	0.0002975	6839896.869	61979.5562
X_STATEND	0.0018995	0.0000182	393524.260	3688.5560
X_STATENE	0.0049175	0.0000371	1018754.973	7405.5986
X_STATENH	0.0037430	0.0000359	775431.885	7278.7846
X_STATENJ	0.0227288	0.0003204	4708720.099	67155.0025
X_STATENM	0.0065569	0.0000563	1358393.331	11379.1077
X_STATENV	0.0084321	0.0001292	1746883.142	26743.0455
X_STATENY	0.0608535	0.0005789	12607022.623	124725.6721
X_STATEOH	0.0387173	0.0002811	8021069.596	57973.0279
X_STATEOK	0.0130539	0.0000970	2704383.255	19505.2923
X_STATEOR	0.0113973	0.0001289	2361181.255	26476.0154
X_STATEPA	0.0412602	0.0003612	8547871.011	75773.8186
X_STATEPR	0.0169807	0.0001437	3517895.013	29314.6353
X_STATERI	0.0030835	0.0000307	638806.652	6242.2208
X_STATESC	0.0180294	0.0001183	3735154.548	23605.9109
X_STATESD	0.0022500	0.0000304	466123.888	6236.5480
X_STATETN	0.0245526	0.0002444	5086571.639	50716.8693
X_STATETX	0.0856470	0.0010609	17743495.731	237855.0026
X_STATEUT	0.0055914	0.0000414	1158373.745	8269.3936
X_STATEVA	0.0254354	0.0002205	5269444.782	45450.4604
X_STATEVI	0.0000419	0.0000029	8674.188	605.1534
X_STATEVT	0.0015639	0.0000155	323991.990	3147.1129
X_STATEWA	0.0188813	0.0001363	3911645.349	27505.3061
X_STATEWI	0.0149629	0.0001701	3099855.976	35146.7022
X_STATEWV	0.0079539	0.0000546	1647802.070	10852.2732
X_STATEWY	0.0014154	0.0000163	293225.583	3323.2178
Year	2015.1914722	0.0062328	417487267006.730	887648073.2181
BldSugarNo	0.1388114	0.0015286	11092658.394	128582.3133
BldSugarYes	0.8611886	0.0015286	68819044.106	282602.6069
DocDiabNo	0.1420431	0.0017199	11350831.460	148324.2842
DocDiabYes	0.8579569	0.0017199	68560330.581	273421.4361
HemChkNo	0.1681733	0.0016941	13439028.437	145443.9769
HemChkYes	0.8318267	0.0016941	66472737.021	277015.5612
FtChkNo	0.2729767	0.0020250	21650378.812	183064.0059
FtChkYes	0.7270233	0.0020250	57661806.762	261201.0145
DiabEdNo	0.4770469	0.0022242	38117168.669	237458.6412
DiabEdYes	0.5229531	0.0022242	41785178.024	223374.2253
EyeExamNo	0.3061043	0.0021286	24459515.446	201395.9225
EyeExamYes	0.6938957	0.0021286	55446296.243	250884.9013
MedExp	1.3256828	0.0047685	274641739.597	1132616.7243
MedExp2No	0.6086932	0.0011421	126102989.756	365426.2548
MedExp2Yes	0.3913068	0.0011421	81067034.914	286388.5162
MaleFemale	0.4948279	0.0015471	102513501.094	373084.6251
MaleMale	0.5051721	0.0015471	104656523.576	404308.0254
MarriedMarried	0.5464005	0.0015300	113197802.782	424362.0401
MarriedDivorced	0.1522456	0.0010659	31540725.830	225695.9316
MarriedWidowed	0.1624575	0.0010446	33656321.194	219886.2654
MarriedSeparated	0.0318433	0.0005582	6596980.735	116298.3605
MarriedNever Married	0.0847754	0.0008600	17562912.129	181419.5559
MarriedUnmarried Couple	0.0177622	0.0004547	3679787.163	94723.4170
MarriedUnknown	0.0045156	0.0001999	935494.837	41440.8331
Age45-54	0.2058291	0.0013542	42641619.508	306664.5580
Age55-64	0.3110713	0.0014598	64444649.619	336964.4853
Age65+	0.4830996	0.0015248	100083755.543	350990.3440
RaceWhite Non-Hispanic	0.6026758	0.0015629	124856368.175	310765.9341
RaceBlack Non-Hispanic	0.1508298	0.0011379	31247409.884	249025.5516
RaceHispanic	0.1586944	0.0013803	32876725.221	312288.3148
RaceOther race only Non-Hispanic	0.0564524	0.0010636	11695235.041	228480.1868
RaceMulti-racial Non-Hispanic	0.0123055	0.0002918	2549337.583	60420.5194
RaceUnknown	0.0190421	0.0003931	3944948.766	81602.4960
Education<=8th	0.0981966	0.0011760	20343401.254	256478.4579
EducationGrades 9-11	0.1256918	0.0011823	26039574.895	261220.8603
EducationGrade 12 / GED	0.3086335	0.0013597	63939615.520	297843.6394
EducationCollege 1-3	0.2881627	0.0013967	59698668.757	314535.2230
EducationCollege 4+	0.1748789	0.0009922	36229662.244	196471.0246
EducationUnknown	0.0044365	0.0002105	919102.000	43656.3130
IncomeLT10K	0.0723430	0.0008298	14987293.210	174955.5228
IncomeLT15K	0.0790430	0.0008552	16375345.516	180812.2280
IncomeLT20K	0.0924451	0.0009054	19151856.563	192205.0355
IncomeLT25K	0.1003437	0.0009186	20788201.705	195099.4958
IncomeLT35K	0.1036388	0.0009434	21470849.460	200987.0190
IncomeLT50K	0.1136600	0.0009282	23546949.997	196422.2708
IncomeLT75K	0.1102460	0.0009538	22839657.312	202738.3589
IncomeGTE75K	0.1596420	0.0011506	33073029.279	248357.4143
IncomeUnknown	0.1686385	0.0011756	34936841.629	257359.9996
EmployEmployed	0.2377904	0.0013552	49263044.186	304241.8912
EmploySelf-Employed	0.0531108	0.0007385	11002968.165	155255.5921
EmployOut of Work GTE 1 Year	0.0310399	0.0006072	6430543.180	127194.4510
EmployOut of Work LT 1 Year	0.0167976	0.0004612	3479968.979	96118.8462
EmployHomemaker	0.0552644	0.0008087	11449119.352	171529.3639
EmployStudent	0.0016534	0.0001365	342524.973	28288.0765
EmployRetired	0.4149663	0.0014769	85968578.612	330143.1111
EmployUnable to Work	0.1808284	0.0012044	37462224.271	263345.1399
EmployUnknown	0.0085488	0.0003368	1771052.952	70006.8629
HlthPlnYes	0.9227795	0.0009693	191172257.456	432698.3298
HlthPlnNo	0.0744841	0.0009616	15430878.271	205585.0522
HlthPlnUnknown	0.0027363	0.0001422	566888.942	29462.8370
PersDocNo	0.0602899	0.0008547	12490262.824	181158.6248
PersDocYes	0.9355948	0.0008773	193827199.329	436405.8338
PersDocUnknown	0.0041153	0.0002162	852562.517	44852.4517
CheckupNo	0.0921869	0.0009454	19098357.932	201564.2033
CheckupYes	0.8935771	0.0009925	185122386.115	438297.4379
CheckupUnknown	0.0142360	0.0003428	2949280.623	71185.8066
MedCostNo	0.8612919	0.0011417	178433859.303	432801.2039
MedCostYes	0.1348323	0.0011297	27933213.684	245350.6358
MedCostUnknown	0.0038758	0.0001990	802951.682	41285.4930
DiabEyeNo	0.3046777	0.0012101	63120092.994	269587.6057
DiabEyeYes	0.0758682	0.0007646	15717620.466	161937.8287
DiabEyeUnknown	0.6194541	0.0012643	128332311.210	399359.8951
GenHlthExcellent	0.0320172	0.0005694	6632821.885	119029.1337
GenHlthVery Good	0.1477155	0.0010342	30601419.648	219085.3282
GenHlthGood	0.3539450	0.0014839	73324875.944	345458.5067
GenHlthFair	0.3011246	0.0014209	62382355.132	321690.3068
GenHlthPoor	0.1600271	0.0011888	33151945.199	260216.8336
GenHlthUnknown	0.0051706	0.0002452	1071169.946	50878.2496
Stratum	234700.9790945	412.8482325	48623007629029.164	137613325336.6352
Weights	2000.3317942	32.4977475	414408787153.513	7328892255.9581

write.csv(temp2, "D:/Diabetes/Descriptives.csv")

Frequencies for DVs

a1=svymean(~BldSugar, svytot, na.rm=TRUE)
a2=svymean(~DocDiab, svytot, na.rm=TRUE)
a3=svymean(~HemChk, svytot, na.rm=TRUE)
a4=svymean(~DiabEd, svytot, na.rm=TRUE)
a5=svymean(~FtChk, svytot, na.rm=TRUE)
a6=svymean(~EyeExam, svytot, na.rm=TRUE)
dvs=rbind(a1,a2,a3,a4,a5,a6)
colnames(dvs)=c('N','Y')
rownames(dvs)=c("BS","Doc", "Hem", "Ed", "Feet", "Eye")
print(dvs)

##              N         Y
## BS   0.1388114 0.8611886
## Doc  0.1420431 0.8579569
## Hem  0.1681733 0.8318267
## Ed   0.4770469 0.5229531
## Feet 0.2729767 0.7270233
## Eye  0.3061043 0.6938957

Frequencies for IVs

b1=svymean(~Age, svytot, na.rm=TRUE)
b2=svymean(~Race, svytot, na.rm=TRUE)
b3=svymean(~Male, svytot, na.rm=TRUE)
b4=svymean(~Married, svytot, na.rm=TRUE)
b5=svymean(~Income, svytot, na.rm=TRUE)
b6=svymean(~Education, svytot, na.rm=TRUE)
b7=svymean(~Employ, svytot, na.rm=TRUE)
b8=svymean(~HlthPln, svytot, na.rm=TRUE)
b9=svymean(~PersDoc, svytot, na.rm=TRUE)
b10=svymean(~MedCost, svytot, na.rm=TRUE)
b11=svymean(~GenHlth, svytot, na.rm=TRUE)

myprint(c(b1, b2, b3, b4, b5, b6, b7, b8, b9, b10, b11))

##                         Age45-54                         Age55-64 
##                      0.205829099                      0.311071303 
##                           Age65+           RaceWhite Non-Hispanic 
##                      0.483099598                      0.602675838 
##           RaceBlack Non-Hispanic                     RaceHispanic 
##                      0.150829783                      0.158694412 
## RaceOther race only Non-Hispanic    RaceMulti-racial Non-Hispanic 
##                      0.056452351                      0.012305533 
##                      RaceUnknown                       MaleFemale 
##                      0.019042083                      0.494827865 
##                         MaleMale                   MarriedMarried 
##                      0.505172135                      0.546400489 
##                  MarriedDivorced                   MarriedWidowed 
##                      0.152245605                      0.162457485 
##                 MarriedSeparated             MarriedNever Married 
##                      0.031843317                      0.084775354 
##          MarriedUnmarried Couple                   MarriedUnknown 
##                      0.017762160                      0.004515590 
##                      IncomeLT10K                      IncomeLT15K 
##                      0.072342962                      0.079043025 
##                      IncomeLT20K                      IncomeLT25K 
##                      0.092445114                      0.100343675 
##                      IncomeLT35K                      IncomeLT50K 
##                      0.103638784                      0.113660024 
##                      IncomeLT75K                     IncomeGTE75K 
##                      0.110245955                      0.159641962 
##                    IncomeUnknown                   Education<=8th 
##                      0.168638497                      0.098196644 
##             EducationGrades 9-11          EducationGrade 12 / GED 
##                      0.125691808                      0.308633528 
##             EducationCollege 1-3              EducationCollege 4+ 
##                      0.288162676                      0.174878882 
##                 EducationUnknown                   EmployEmployed 
##                      0.004436462                      0.237790406 
##              EmploySelf-Employed     EmployOut of Work GTE 1 Year 
##                      0.053110812                      0.031039931 
##      EmployOut of Work LT 1 Year                  EmployHomemaker 
##                      0.016797647                      0.055264363 
##                    EmployStudent                    EmployRetired 
##                      0.001653352                      0.414966300 
##             EmployUnable to Work                    EmployUnknown 
##                      0.180828401                      0.008548790 
##                       HlthPlnYes                        HlthPlnNo 
##                      0.922779527                      0.074484126 
##                   HlthPlnUnknown                        PersDocNo 
##                      0.002736346                      0.060289913 
##                       PersDocYes                   PersDocUnknown 
##                      0.935594807                      0.004115279 
##                        MedCostNo                       MedCostYes 
##                      0.861291877                      0.134832313 
##                   MedCostUnknown                 GenHlthExcellent 
##                      0.003875810                      0.032017161 
##                 GenHlthVery Good                      GenHlthGood 
##                      0.147715495                      0.353945029 
##                      GenHlthFair                      GenHlthPoor 
##                      0.301124607                      0.160027086 
##                   GenHlthUnknown 
##                      0.005170623

	x
Age45-54	0.2058291
Age55-64	0.3110713
Age65+	0.4830996
RaceWhite Non-Hispanic	0.6026758
RaceBlack Non-Hispanic	0.1508298
RaceHispanic	0.1586944
RaceOther race only Non-Hispanic	0.0564524
RaceMulti-racial Non-Hispanic	0.0123055
RaceUnknown	0.0190421
MaleFemale	0.4948279
MaleMale	0.5051721
MarriedMarried	0.5464005
MarriedDivorced	0.1522456
MarriedWidowed	0.1624575
MarriedSeparated	0.0318433
MarriedNever Married	0.0847754
MarriedUnmarried Couple	0.0177622
MarriedUnknown	0.0045156
IncomeLT10K	0.0723430
IncomeLT15K	0.0790430
IncomeLT20K	0.0924451
IncomeLT25K	0.1003437
IncomeLT35K	0.1036388
IncomeLT50K	0.1136600
IncomeLT75K	0.1102460
IncomeGTE75K	0.1596420
IncomeUnknown	0.1686385
Education<=8th	0.0981966
EducationGrades 9-11	0.1256918
EducationGrade 12 / GED	0.3086335
EducationCollege 1-3	0.2881627
EducationCollege 4+	0.1748789
EducationUnknown	0.0044365
EmployEmployed	0.2377904
EmploySelf-Employed	0.0531108
EmployOut of Work GTE 1 Year	0.0310399
EmployOut of Work LT 1 Year	0.0167976
EmployHomemaker	0.0552644
EmployStudent	0.0016534
EmployRetired	0.4149663
EmployUnable to Work	0.1808284
EmployUnknown	0.0085488
HlthPlnYes	0.9227795
HlthPlnNo	0.0744841
HlthPlnUnknown	0.0027363
PersDocNo	0.0602899
PersDocYes	0.9355948
PersDocUnknown	0.0041153
MedCostNo	0.8612919
MedCostYes	0.1348323
MedCostUnknown	0.0038758
GenHlthExcellent	0.0320172
GenHlthVery Good	0.1477155
GenHlthGood	0.3539450
GenHlthFair	0.3011246
GenHlthPoor	0.1600271
GenHlthUnknown	0.0051706

Time Series by Variable

dv1=round(svyby(~DiabEd,~Year,svytot, svymean, na.rm=TRUE),3)
dv2=round(svyby(~EyeExam,~Year,svytot, svymean, na.rm=TRUE),3)#dich
dv3=round(svyby(~BldSugar,~Year,svytot, svymean, na.rm=TRUE),3)#dich
dv4=round(svyby(~DocDiab,~Year,svytot, svymean, na.rm=TRUE),3)#dich
dv5=round(svyby(~FtChk,~Year,svytot, svymean, na.rm=TRUE),3)#dich
dv6=round(svyby(~HemChk,~Year,svytot, svymean, na.rm=TRUE),3)#dich

myprint(dv1)

##      Year DiabEdNo DiabEdYes se.DiabEdNo se.DiabEdYes
## 2011 2011    0.480     0.520       0.008        0.008
## 2012 2012    0.463     0.537       0.005        0.005
## 2013 2013    0.466     0.534       0.005        0.005
## 2014 2014    0.501     0.499       0.006        0.006
## 2015 2015    0.463     0.537       0.005        0.005
## 2016 2016    0.519     0.481       0.009        0.009
## 2017 2017    0.473     0.527       0.006        0.006
## 2018 2018    0.506     0.494       0.010        0.010
## 2019 2019    0.469     0.531       0.007        0.007

	Year	DiabEdNo	DiabEdYes	se.DiabEdNo	se.DiabEdYes
2011	2011	0.480	0.520	0.008	0.008
2012	2012	0.463	0.537	0.005	0.005
2013	2013	0.466	0.534	0.005	0.005
2014	2014	0.501	0.499	0.006	0.006
2015	2015	0.463	0.537	0.005	0.005
2016	2016	0.519	0.481	0.009	0.009
2017	2017	0.473	0.527	0.006	0.006
2018	2018	0.506	0.494	0.010	0.010
2019	2019	0.469	0.531	0.007	0.007

myprint(dv2)

##      Year EyeExamNo EyeExamYes se.EyeExamNo se.EyeExamYes
## 2011 2011     0.285      0.715        0.007         0.007
## 2012 2012     0.308      0.692        0.004         0.004
## 2013 2013     0.316      0.684        0.005         0.005
## 2014 2014     0.323      0.677        0.005         0.005
## 2015 2015     0.302      0.698        0.005         0.005
## 2016 2016     0.313      0.687        0.008         0.008
## 2017 2017     0.298      0.702        0.006         0.006
## 2018 2018     0.331      0.669        0.011         0.011
## 2019 2019     0.283      0.717        0.006         0.006

	Year	EyeExamNo	EyeExamYes	se.EyeExamNo	se.EyeExamYes
2011	2011	0.285	0.715	0.007	0.007
2012	2012	0.308	0.692	0.004	0.004
2013	2013	0.316	0.684	0.005	0.005
2014	2014	0.323	0.677	0.005	0.005
2015	2015	0.302	0.698	0.005	0.005
2016	2016	0.313	0.687	0.008	0.008
2017	2017	0.298	0.702	0.006	0.006
2018	2018	0.331	0.669	0.011	0.011
2019	2019	0.283	0.717	0.006	0.006

myprint(dv3)

##      Year BldSugarNo BldSugarYes se.BldSugarNo se.BldSugarYes
## 2011 2011      0.122       0.878         0.005          0.005
## 2012 2012      0.125       0.875         0.003          0.003
## 2013 2013      0.132       0.868         0.004          0.004
## 2014 2014      0.135       0.865         0.004          0.004
## 2015 2015      0.145       0.855         0.004          0.004
## 2016 2016      0.150       0.850         0.006          0.006
## 2017 2017      0.161       0.839         0.004          0.004
## 2018 2018      0.130       0.870         0.006          0.006
## 2019 2019      0.133       0.867         0.005          0.005

	Year	BldSugarNo	BldSugarYes	se.BldSugarNo	se.BldSugarYes
2011	2011	0.122	0.878	0.005	0.005
2012	2012	0.125	0.875	0.003	0.003
2013	2013	0.132	0.868	0.004	0.004
2014	2014	0.135	0.865	0.004	0.004
2015	2015	0.145	0.855	0.004	0.004
2016	2016	0.150	0.850	0.006	0.006
2017	2017	0.161	0.839	0.004	0.004
2018	2018	0.130	0.870	0.006	0.006
2019	2019	0.133	0.867	0.005	0.005

myprint(dv4)

##      Year DocDiabNo DocDiabYes se.DocDiabNo se.DocDiabYes
## 2011 2011     0.142      0.858        0.005         0.005
## 2012 2012     0.146      0.854        0.003         0.003
## 2013 2013     0.134      0.866        0.004         0.004
## 2014 2014     0.131      0.869        0.004         0.004
## 2015 2015     0.148      0.852        0.004         0.004
## 2016 2016     0.134      0.866        0.006         0.006
## 2017 2017     0.138      0.862        0.004         0.004
## 2018 2018     0.146      0.854        0.010         0.010
## 2019 2019     0.156      0.844        0.005         0.005

	Year	DocDiabNo	DocDiabYes	se.DocDiabNo	se.DocDiabYes
2011	2011	0.142	0.858	0.005	0.005
2012	2012	0.146	0.854	0.003	0.003
2013	2013	0.134	0.866	0.004	0.004
2014	2014	0.131	0.869	0.004	0.004
2015	2015	0.148	0.852	0.004	0.004
2016	2016	0.134	0.866	0.006	0.006
2017	2017	0.138	0.862	0.004	0.004
2018	2018	0.146	0.854	0.010	0.010
2019	2019	0.156	0.844	0.005	0.005

myprint(dv5)

##      Year FtChkNo FtChkYes se.FtChkNo se.FtChkYes
## 2011 2011   0.270    0.730      0.007       0.007
## 2012 2012   0.292    0.708      0.004       0.004
## 2013 2013   0.289    0.711      0.005       0.005
## 2014 2014   0.297    0.703      0.005       0.005
## 2015 2015   0.247    0.753      0.005       0.005
## 2016 2016   0.293    0.707      0.008       0.008
## 2017 2017   0.258    0.742      0.005       0.005
## 2018 2018   0.292    0.708      0.010       0.010
## 2019 2019   0.245    0.755      0.006       0.006

	Year	FtChkNo	FtChkYes	se.FtChkNo	se.FtChkYes
2011	2011	0.270	0.730	0.007	0.007
2012	2012	0.292	0.708	0.004	0.004
2013	2013	0.289	0.711	0.005	0.005
2014	2014	0.297	0.703	0.005	0.005
2015	2015	0.247	0.753	0.005	0.005
2016	2016	0.293	0.707	0.008	0.008
2017	2017	0.258	0.742	0.005	0.005
2018	2018	0.292	0.708	0.010	0.010
2019	2019	0.245	0.755	0.006	0.006

myprint(dv6)

##      Year HemChkNo HemChkYes se.HemChkNo se.HemChkYes
## 2011 2011    0.210     0.790       0.007        0.007
## 2012 2012    0.203     0.797       0.004        0.004
## 2013 2013    0.206     0.794       0.004        0.004
## 2014 2014    0.180     0.820       0.005        0.005
## 2015 2015    0.152     0.848       0.004        0.004
## 2016 2016    0.154     0.846       0.006        0.006
## 2017 2017    0.139     0.861       0.004        0.004
## 2018 2018    0.156     0.844       0.007        0.007
## 2019 2019    0.139     0.861       0.005        0.005

	Year	HemChkNo	HemChkYes	se.HemChkNo	se.HemChkYes
2011	2011	0.210	0.790	0.007	0.007
2012	2012	0.203	0.797	0.004	0.004
2013	2013	0.206	0.794	0.004	0.004
2014	2014	0.180	0.820	0.005	0.005
2015	2015	0.152	0.848	0.004	0.004
2016	2016	0.154	0.846	0.006	0.006
2017	2017	0.139	0.861	0.004	0.004
2018	2018	0.156	0.844	0.007	0.007
2019	2019	0.139	0.861	0.005	0.005

Dependent Variable Tables

round(svyby(~BldSugar,~as.numeric(MedExp2),svytot, svytotal, na.rm=TRUE),3)#dich

##   as.numeric(MedExp2) BldSugarNo BldSugarYes se.BldSugarNo se.BldSugarYes
## 1                   1    7359349    47801008     111724.78       252618.7
## 2                   2    3733309    21018036      64260.78       136841.5

round(svyby(~DocDiab,~as.numeric(MedExp2),svytot, svytotal, na.rm=TRUE),3)#dich

##   as.numeric(MedExp2) DocDiabNo DocDiabYes se.DocDiabNo se.DocDiabYes
## 1                   1   7861473   47297491    134580.60      242134.7
## 2                   2   3489358   21262840     62976.61      137145.8

round(svyby(~HemChk,~as.numeric(MedExp2),svytot, svytotal, na.rm=TRUE),3)#dich

##   as.numeric(MedExp2) HemChkNo HemChkYes se.HemChkNo se.HemChkYes
## 1                   1  9948491  45211364   129826.52     246976.2
## 2                   2  3490538  21261373    66279.99     135439.3

round(svyby(~DiabEd,~as.numeric(MedExp2),svytot, svytotal, na.rm=TRUE),3)#dich

##   as.numeric(MedExp2) DiabEdNo DiabEdYes se.DiabEdNo se.DiabEdYes
## 1                   1 26474660  28679790    210019.9     198587.6
## 2                   2 11642509  13105388    114605.0     107087.9

round(svyby(~FtChk,~as.numeric(MedExp2),svytot, svytotal, na.rm=TRUE),3)#dich

##   as.numeric(MedExp2)  FtChkNo FtChkYes se.FtChkNo se.FtChkYes
## 1                   1 15482585 39265563  163616.58    231593.9
## 2                   2  6167793 18396244   83602.96    128706.1

round(svyby(~EyeExam,~as.numeric(MedExp2),svytot, svytotal, na.rm=TRUE),3)#dich

##   as.numeric(MedExp2) EyeExamNo EyeExamYes se.EyeExamNo se.EyeExamYes
## 1                   1  17290131   37866119    182102.42      220164.8
## 2                   2   7169384   17580177     87913.88      127635.3

year=seq(2011,2019)
temp1=svyby(~as.numeric(BldSugar), ~Year, svytot, svymean, na.rm=TRUE)[,2]-1
temp2=svyby(~as.numeric(DocDiab), ~Year, svytot, svymean, na.rm=TRUE)[,2]-1
temp3=svyby(~as.numeric(HemChk), ~Year, svytot, svymean, na.rm=TRUE)[,2]-1
temp4=svyby(~as.numeric(DiabEd), ~Year, svytot, svymean, na.rm=TRUE)[,2]-1
temp5=svyby(~as.numeric(FtChk), ~Year, svytot, svymean, na.rm=TRUE)[,2]-1
temp6=svyby(~as.numeric(EyeExam), ~Year, svytot, svymean, na.rm=TRUE)[,2]-1

tempdf=as.data.frame(cbind(year, temp1, temp2, temp3, temp4, temp5, temp6))
colnames(tempdf)=c("Year", "Blood Sugar", "Doctor Visit", "HbA1c Checked", 
                   "Diabetes Education", "Feet Checked", "Eye Exam")

Time Plots

ggdata=data.frame(x = tempdf$Year,                                                   y = c(tempdf$`Blood Sugar`, tempdf$`Doctor Visit`, tempdf$`HbA1c Checked`,      tempdf$`Diabetes Education`, tempdf$`Feet Checked`, tempdf$`Eye Exam`),
     Variable = c(rep("Blood Sugar", nrow(tempdf)),
               rep("Doctor Visit", nrow(tempdf)),
               rep("HbA1c Checked", nrow(tempdf)),
               rep("Diabetes Education", nrow(tempdf)),
               rep("Feet Checked", nrow(tempdf)),
               rep("Eyes Checked", nrow(tempdf))
               ))
mymeans=colMeans
myplot=ggplot(ggdata, aes(x, y, col = Variable)) +
  geom_line()+
  xlab("Year")+
  ylab("%")+
  geom_smooth()+
  geom_text(aes(label=round(y,2)),hjust=1.1, vjust=1, size=3)+
  facet_grid(Variable~.)+
  theme(strip.background = element_blank(),strip.text.y = element_blank())+
  scale_x_continuous(name="Year", breaks=c(2011,2012,2013,2014,2015,2016,2017,2018,2019))

myplot

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

Quasi-Binomial

Full Models by Year and Overall

a1="~Age+
relevel(Male, ref='Female')+
relevel(Race, ref='White Non-Hispanic')+ 
relevel(Married, ref='Married')+
relevel(Income, ref='GTE75K')+
relevel(Education, ref='College 4+')+
relevel(Employ, ref='Employed')+
relevel(GenHlth, ref='Excellent')+
relevel(HlthPln, ref='Yes')+
relevel(PersDoc, ref='Yes')+
relevel(Checkup, ref='Yes')+
relevel(MedCost, ref='No')+
MedExp"


mylog= function(DV){
  myformula3=as.formula(paste(DV,a1))
  m1=svyglm(myformula3,svytot,family=quasibinomial,maxit = 100)
  return(m1)   
}

bs=mylog("BldSugar")
doc=mylog("DocDiab")
hem=mylog("HemChk")
feet=mylog("FtChk")
edu=mylog("DiabEd")
eye=mylog("EyeExam")

names(bs$coefficients)=names(doc$coefficients)=
  names(hem$coefficients)=names(feet$coefficients)=
  names(edu$coefficients)=names(eye$coefficients)=
  c('Intercept','Age 55-64','Age 65+','Male',
    'Black NH','Hispanic','Other Race','Multiracial NH','Unk.Race', 
    'Divorced','Widowed','Separated','Never Married','Unmarried Couple','Unk. Marital',
    'LT $10K','LT $15K','LT $20K','LT $25K','LT $30K','LT $50K','LT $75K','Unk. Income',
    'Grade LTE 8','Grades 9-11','Grade 12','1-3 College','Unk. Education',
    'Self-Employed','Unemployed GTE Yr','Unemployed LT Yr','Homemaker','Student','Retired','Unable to Work','Unk. Employ',
    'Very Good Health','Good Health','Fair Health','Poor Health','Unk. Health Status',
    'No Health Plan','Unk. Health Plan',
    'No Personal Doctor', 'Unk. Personal Doc',
    'No Checkup','Unk. Checkup', 
    'Cost Impacted Care','Unk Cost', 
    'Medicaid Expansion'
)

Demographic only

a10="~Age+
relevel(Male, ref='Female')+
relevel(Race, ref='White Non-Hispanic')+ 
relevel(Married, ref='Married')"


mylog= function(DV){
  myformula3=as.formula(paste(DV,a10))
  m1=svyglm(myformula3,svytot,family=quasibinomial,maxit = 100)
  return(m1)   
}

bs0=mylog("BldSugar")
doc0=mylog("DocDiab")
hem0=mylog("HemChk")
feet0=mylog("FtChk")
edu0=mylog("DiabEd")
eye0=mylog("EyeExam")

names(bs0$coefficients)=names(doc0$coefficients)=
  names(hem0$coefficients)=names(feet0$coefficients)=
  names(edu0$coefficients)=names(eye0$coefficients)=
  c('Intercept','Age 55-64','Age 65+','Male',
    'Black NH','Hispanic','Other Race','Multiracial NH','Unk.Race', 
    'Divorced','Widowed','Separated','Never Married','Unmarried Couple','Unk. Marital'
)

Demographics + Socioeconomics

a11="~Age+
relevel(Male, ref='Female')+
relevel(Race, ref='White Non-Hispanic')+ 
relevel(Married, ref='Married')+
relevel(Income, ref='GTE75K')+
relevel(Education, ref='College 4+')+
relevel(Employ, ref='Employed')"


mylog= function(DV){
  myformula3=as.formula(paste(DV,a11))
  m1=svyglm(myformula3,svytot,family=quasibinomial,maxit = 100)
  return(m1)   
}

bs1=mylog("BldSugar")
doc1=mylog("DocDiab")
hem1=mylog("HemChk")
feet1=mylog("FtChk")
edu1=mylog("DiabEd")
eye1=mylog("EyeExam")

names(bs1$coefficients)=names(doc1$coefficients)=
  names(hem1$coefficients)=names(feet1$coefficients)=
  names(edu1$coefficients)=names(eye1$coefficients)=
  c('Intercept','Age 55-64','Age 65+','Male',
    'Black NH','Hispanic','Other Race','Multiracial NH','Unk.Race', 
    'Divorced','Widowed','Separated','Never Married','Unmarried Couple','Unk. Marital',
    'LT $10K','LT $15K','LT $20K','LT $25K','LT $30K','LT $50K','LT $75K','Unk. Income',
    'Grade LTE 8','Grades 9-11','Grade 12','1-3 College','Unk. Education',
    'Self-Employed','Unemployed GTE Yr','Unemployed LT Yr','Homemaker','Student','Retired','Unable to Work','Unk. Employ'
 
)

Socioeconomics+ Health Status

a16="~
relevel(Income, ref='GTE75K')+
relevel(Education, ref='College 4+')+
relevel(Employ, ref='Employed')+
relevel(GenHlth, ref='Excellent')+
relevel(HlthPln, ref='Yes')+
relevel(PersDoc, ref='Yes')+
relevel(Checkup, ref='Yes')+
relevel(MedCost, ref='No')"


mylog= function(DV){
  myformula3=as.formula(paste(DV,a16))
  m1=svyglm(myformula3,svytot,family=quasibinomial,maxit = 100)
  return(m1)   
}

bs6=mylog("BldSugar")
doc6=mylog("DocDiab")
hem6=mylog("HemChk")
feet6=mylog("FtChk")
edu6=mylog("DiabEd")
eye6=mylog("EyeExam")

names(bs6$coefficients)=names(doc6$coefficients)=
  names(hem6$coefficients)=names(feet6$coefficients)=
  names(edu6$coefficients)=names(eye6$coefficients)=
  c('Intercept',
    'LT $10K','LT $15K','LT $20K','LT $25K','LT $30K','LT $50K','LT $75K','Unk. Income',
    'Grade LTE 8','Grades 9-11','Grade 12','1-3 College','Unk. Education',
    'Self-Employed','Unemployed GTE Yr','Unemployed LT Yr','Homemaker','Student','Retired','Unable to Work','Unk. Employ',
    'Very Good Health','Good Health','Fair Health','Poor Health','Unk. Health Status',
    'No Health Plan','Unk. Health Plan',
    'No Personal Doctor', 'Unk. Personal Doc',
    'No Checkup','Unk. Checkup', 
    'Cost Impacted Care','Unk Cost'
)

Demographics + Health Status

a17="~Age+
relevel(Male, ref='Female')+
relevel(Race, ref='White Non-Hispanic')+ 
relevel(Married, ref='Married')+
relevel(GenHlth, ref='Excellent')+
relevel(HlthPln, ref='Yes')+
relevel(PersDoc, ref='Yes')+
relevel(Checkup, ref='Yes')+
relevel(MedCost, ref='No')"


mylog= function(DV){
  myformula3=as.formula(paste(DV,a17))
  m1=svyglm(myformula3,svytot,family=quasibinomial,maxit = 100)
  return(m1)   
}

bs7=mylog("BldSugar")
doc7=mylog("DocDiab")
hem7=mylog("HemChk")
feet7=mylog("FtChk")
edu7=mylog("DiabEd")
eye7=mylog("EyeExam")

names(bs7$coefficients)=names(doc7$coefficients)=
  names(hem7$coefficients)=names(feet7$coefficients)=
  names(edu7$coefficients)=names(eye7$coefficients)=
  c('Intercept','Age 55-64','Age 65+','Male',
    'Black NH','Hispanic','Other Race','Multiracial NH','Unk.Race', 
    'Divorced','Widowed','Separated','Never Married','Unmarried Couple','Unk. Marital',
    'Very Good Health','Good Health','Fair Health','Poor Health','Unk. Health Status',
    'No Health Plan','Unk. Health Plan',
    'No Personal Doctor', 'Unk. Personal Doc',
    'No Checkup','Unk. Checkup', 
    'Cost Impacted Care','Unk Cost'
)

Health Status + Expansion

a20="~Age+
relevel(GenHlth, ref='Excellent')+
relevel(HlthPln, ref='Yes')+
relevel(PersDoc, ref='Yes')+
relevel(Checkup, ref='Yes')+
relevel(MedCost, ref='No')+
MedExp"


mylog= function(DV){
  myformula3=as.formula(paste(DV,a20))
  m1=svyglm(myformula3,svytot,family=quasibinomial,maxit = 100)
  return(m1)   
}

bs20=mylog("BldSugar")
doc20=mylog("DocDiab")
hem20=mylog("HemChk")
feet20=mylog("FtChk")
edu20=mylog("DiabEd")
eye20=mylog("EyeExam")

names(bs20$coefficients)=names(doc20$coefficients)=
  names(hem20$coefficients)=names(feet20$coefficients)=
  names(edu20$coefficients)=names(eye20$coefficients)=
  c('Intercept',
    'Very Good Health','Good Health','Fair Health','Poor Health','Unk. Health Status',
    'No Health Plan','Unk. Health Plan',
    'No Personal Doctor', 'Unk. Personal Doc',
    'No Checkup','Unk. Checkup', 
    'Cost Impacted Care','Unk Cost', 'Medicaid Expansion'
)

Demographics + Expansion

a18="~Age+
relevel(Male, ref='Female')+
relevel(Race, ref='White Non-Hispanic')+ 
relevel(Married, ref='Married')+
MedExp"


mylog= function(DV){
  myformula3=as.formula(paste(DV,a18))
  m1=svyglm(myformula3,svytot,family=quasibinomial,maxit = 100)
  return(m1)   
}

bs8=mylog("BldSugar")
doc8=mylog("DocDiab")
hem8=mylog("HemChk")
feet8=mylog("FtChk")
edu8=mylog("DiabEd")
eye8=mylog("EyeExam")

names(bs8$coefficients)=names(doc8$coefficients)=
  names(hem8$coefficients)=names(feet8$coefficients)=
  names(edu8$coefficients)=names(eye8$coefficients)=
  c('Intercept','Age 55-64','Age 65+','Male',
    'Black NH','Hispanic','Other Race','Multiracial NH','Unk.Race', 
    'Divorced','Widowed','Separated','Never Married','Unmarried Couple','Unk. Marital','Medicaid Expansion'
)

Socioeconomics + Expansion

a19="~
relevel(Income, ref='GTE75K')+
relevel(Education, ref='College 4+')+
relevel(Employ, ref='Employed')+
MedExp"


mylog= function(DV){
  myformula3=as.formula(paste(DV,a19))
  m1=svyglm(myformula3,svytot,family=quasibinomial,maxit = 100)
  return(m1)   
}

bs9=mylog("BldSugar")
doc9=mylog("DocDiab")
hem9=mylog("HemChk")
feet9=mylog("FtChk")
edu9=mylog("DiabEd")
eye9=mylog("EyeExam")

names(bs9$coefficients)=names(doc9$coefficients)=
  names(hem9$coefficients)=names(feet9$coefficients)=
  names(edu9$coefficients)=names(eye9$coefficients)=
  c('Intercept',
    'LT $10K','LT $15K','LT $20K','LT $25K','LT $30K','LT $50K','LT $75K','Unk. Income',
    'Grade LTE 8','Grades 9-11','Grade 12','1-3 College','Unk. Education',
    'Self-Employed','Unemployed GTE Yr','Unemployed LT Yr','Homemaker','Student','Retired','Unable to Work','Unk. Employ',
'Medicaid Expansion'
)

Demographics + Socioeconomics + Health Status

a12="~Age+
relevel(Male, ref='Female')+
relevel(Race, ref='White Non-Hispanic')+ 
relevel(Married, ref='Married')+
relevel(Income, ref='GTE75K')+
relevel(Education, ref='College 4+')+
relevel(Employ, ref='Employed')+
relevel(GenHlth, ref='Excellent')+
relevel(HlthPln, ref='Yes')+
relevel(PersDoc, ref='Yes')+
relevel(Checkup, ref='Yes')+
relevel(MedCost, ref='No')"


mylog= function(DV){
  myformula3=as.formula(paste(DV,a12))
  m1=svyglm(myformula3,svytot,family=quasibinomial,maxit = 100)
  return(m1)   
}

bs2=mylog("BldSugar")
doc2=mylog("DocDiab")
hem2=mylog("HemChk")
feet2=mylog("FtChk")
edu2=mylog("DiabEd")
eye2=mylog("EyeExam")

names(bs2$coefficients)=names(doc2$coefficients)=
  names(hem2$coefficients)=names(feet2$coefficients)=
  names(edu2$coefficients)=names(eye2$coefficients)=
  c('Intercept','Age 55-64','Age 65+','Male',
    'Black NH','Hispanic','Other Race','Multiracial NH','Unk.Race', 
    'Divorced','Widowed','Separated','Never Married','Unmarried Couple','Unk. Marital',
    'LT $10K','LT $15K','LT $20K','LT $25K','LT $30K','LT $50K','LT $75K','Unk. Income',
    'Grade LTE 8','Grades 9-11','Grade 12','1-3 College','Unk. Education',
    'Self-Employed','Unemployed GTE Yr','Unemployed LT Yr','Homemaker','Student','Retired','Unable to Work','Unk. Employ',
    'Very Good Health','Good Health','Fair Health','Poor Health','Unk. Health Status',
    'No Health Plan','Unk. Health Plan',
    'No Personal Doctor', 'Unk. Personal Doc',
    'No Checkup','Unk. Checkup', 
    'Cost Impacted Care','Unk Cost'
)

Demographics + Socioeconomics + Expansion

a21="~Age+
relevel(Male, ref='Female')+
relevel(Race, ref='White Non-Hispanic')+ 
relevel(Married, ref='Married')+
relevel(Income, ref='GTE75K')+
relevel(Education, ref='College 4+')+
relevel(Employ, ref='Employed')+
MedExp"


mylog= function(DV){
  myformula3=as.formula(paste(DV,a21))
  m1=svyglm(myformula3,svytot,family=quasibinomial,maxit = 100)
  return(m1)   
}

bs21=mylog("BldSugar")
doc21=mylog("DocDiab")
hem21=mylog("HemChk")
feet21=mylog("FtChk")
edu21=mylog("DiabEd")
eye21=mylog("EyeExam")

names(bs21$coefficients)=names(doc21$coefficients)=
  names(hem21$coefficients)=names(feet21$coefficients)=
  names(edu21$coefficients)=names(eye21$coefficients)=
  c('Intercept','Age 55-64','Age 65+','Male',
    'Black NH','Hispanic','Other Race','Multiracial NH','Unk.Race', 
    'Divorced','Widowed','Separated','Never Married','Unmarried Couple','Unk. Marital',
    'LT $10K','LT $15K','LT $20K','LT $25K','LT $30K','LT $50K','LT $75K','Unk. Income',
    'Grade LTE 8','Grades 9-11','Grade 12','1-3 College','Unk. Education',
    'Self-Employed','Unemployed GTE Yr','Unemployed LT Yr','Homemaker','Student','Retired','Unable to Work','Unk. Employ',
    'Medicaid Expansion'
)

Demographics + Health Status + Expansion

a22="~Age+
relevel(Male, ref='Female')+
relevel(Race, ref='White Non-Hispanic')+ 
relevel(Married, ref='Married')+
relevel(GenHlth, ref='Excellent')+
relevel(HlthPln, ref='Yes')+
relevel(PersDoc, ref='Yes')+
relevel(Checkup, ref='Yes')+
relevel(MedCost, ref='No')+
MedExp"


mylog= function(DV){
  myformula3=as.formula(paste(DV,a22))
  m1=svyglm(myformula3,svytot,family=quasibinomial,maxit = 100)
  return(m1)   
}

bs22=mylog("BldSugar")
doc22=mylog("DocDiab")
hem22=mylog("HemChk")
feet22=mylog("FtChk")
edu22=mylog("DiabEd")
eye22=mylog("EyeExam")

names(bs22$coefficients)=names(doc22$coefficients)=
  names(hem22$coefficients)=names(feet22$coefficients)=
  names(edu22$coefficients)=names(eye22$coefficients)=
  c('Intercept','Age 55-64','Age 65+','Male',
    'Black NH','Hispanic','Other Race','Multiracial NH','Unk.Race', 
    'Divorced','Widowed','Separated','Never Married','Unmarried Couple','Unk. Marital',
    'Very Good Health','Good Health','Fair Health','Poor Health','Unk. Health Status',
    'No Health Plan','Unk. Health Plan',
    'No Personal Doctor', 'Unk. Personal Doc',
    'No Checkup','Unk. Checkup', 
    'Cost Impacted Care','Unk Cost', 'Medicaid Expansion'
)

Socioeconomics + Health Status + Expansion

a23="~
relevel(Income, ref='GTE75K')+
relevel(Education, ref='College 4+')+
relevel(Employ, ref='Employed')+
relevel(GenHlth, ref='Excellent')+
relevel(HlthPln, ref='Yes')+
relevel(PersDoc, ref='Yes')+
relevel(Checkup, ref='Yes')+
relevel(MedCost, ref='No')+
MedExp"


mylog= function(DV){
  myformula3=as.formula(paste(DV,a23))
  m1=svyglm(myformula3,svytot,family=quasibinomial,maxit = 100)
  return(m1)   
}

bs23=mylog("BldSugar")
doc23=mylog("DocDiab")
hem23=mylog("HemChk")
feet23=mylog("FtChk")
edu23=mylog("DiabEd")
eye23=mylog("EyeExam")

names(bs23$coefficients)=names(doc23$coefficients)=
  names(hem23$coefficients)=names(feet23$coefficients)=
  names(edu23$coefficients)=names(eye23$coefficients)=
  c('Intercept',
    'LT $10K','LT $15K','LT $20K','LT $25K','LT $30K','LT $50K','LT $75K','Unk. Income',
    'Grade LTE 8','Grades 9-11','Grade 12','1-3 College','Unk. Education',
    'Self-Employed','Unemployed GTE Yr','Unemployed LT Yr','Homemaker','Student','Retired','Unable to Work','Unk. Employ',
    'Very Good Health','Good Health','Fair Health','Poor Health','Unk. Health Status',
    'No Health Plan','Unk. Health Plan',
    'No Personal Doctor', 'Unk. Personal Doc',
    'No Checkup','Unk. Checkup', 
    'Cost Impacted Care','Unk Cost', 'Medicaid Expansion'
)

Medicaid Expansion Only

a13="~MedExp"


mylog= function(DV){
  myformula3=as.formula(paste(DV,a13))
  m1=svyglm(myformula3,svytot,family=quasibinomial,maxit = 100)
  return(m1)   
}

bs3=mylog("BldSugar")
doc3=mylog("DocDiab")
hem3=mylog("HemChk")
feet3=mylog("FtChk")
edu3=mylog("DiabEd")
eye3=mylog("EyeExam")

names(bs3$coefficients)=names(doc3$coefficients)=
  names(hem3$coefficients)=names(feet3$coefficients)=
  names(edu3$coefficients)=names(eye3$coefficients)=
  c('Intercept','Medicaid Expansion')

Socioeconomics Only

a14="~
relevel(Income, ref='GTE75K')+
relevel(Education, ref='College 4+')+
relevel(Employ, ref='Employed')"


mylog= function(DV){
  myformula3=as.formula(paste(DV,a14))
  m1=svyglm(myformula3,svytot,family=quasibinomial,maxit = 100)
  return(m1)   
}

bs4=mylog("BldSugar")
doc4=mylog("DocDiab")
hem4=mylog("HemChk")
feet4=mylog("FtChk")
edu4=mylog("DiabEd")
eye4=mylog("EyeExam")

names(bs4$coefficients)=names(doc4$coefficients)=
  names(hem4$coefficients)=names(feet4$coefficients)=
  names(edu4$coefficients)=names(eye4$coefficients)=
  c('Intercept',
    'LT $10K','LT $15K','LT $20K','LT $25K','LT $30K','LT $50K','LT $75K','Unk. Income',
    'Grade LTE 8','Grades 9-11','Grade 12','1-3 College','Unk. Education',
    'Self-Employed','Unemployed GTE Yr','Unemployed LT Yr','Homemaker','Student','Retired','Unable to Work','Unk. Employ'
)

Health Status Only

a15="~
relevel(GenHlth, ref='Excellent')+
relevel(HlthPln, ref='Yes')+
relevel(PersDoc, ref='Yes')+
relevel(Checkup, ref='Yes')+
relevel(MedCost, ref='No')"


mylog= function(DV){
  myformula3=as.formula(paste(DV,a15))
  m1=svyglm(myformula3,svytot,family=quasibinomial,maxit = 100)
  return(m1)   
}

bs5=mylog("BldSugar")
doc5=mylog("DocDiab")
hem5=mylog("HemChk")
feet5=mylog("FtChk")
edu5=mylog("DiabEd")
eye5=mylog("EyeExam")

names(bs5$coefficients)=names(doc5$coefficients)=
  names(hem5$coefficients)=names(feet5$coefficients)=
  names(edu5$coefficients)=names(eye5$coefficients)=
  c('Intercept',
    'Very Good Health','Good Health','Fair Health','Poor Health','Unk. Health Status',
    'No Health Plan','Unk. Health Plan',
    'No Personal Doctor', 'Unk. Personal Doc',
    'No Checkup','Unk. Checkup', 
    'Cost Impacted Care','Unk Cost'
)

AIC Comparison

q1=cbind(AIC(bs0)[2], AIC(bs4)[2],AIC(bs5)[2],AIC(bs3)[2],
         AIC(bs1)[2], AIC(bs6)[2],AIC(bs7)[2],AIC(bs8)[2],
         AIC(bs9)[2], AIC(bs20)[2],
         AIC(bs2)[2], AIC(bs21)[2], AIC(bs22)[2], AIC(bs23)[2],
         AIC(bs)[2])
q2=cbind(AIC(doc0)[2], AIC(doc4)[2],AIC(doc5)[2],AIC(doc3)[2],
         AIC(doc1)[2],AIC(doc6)[2],AIC(doc7)[2],AIC(doc8)[2],
         AIC(doc9)[2],AIC(doc20)[2],
         AIC(doc2)[2],  AIC(doc21)[2],AIC(doc22)[2],  AIC(doc23)[2],
         AIC(doc)[2])
q3=cbind(AIC(hem0)[2], AIC(hem4)[2],AIC(hem5)[2],AIC(hem3)[2],
         AIC(hem1)[2],  AIC(hem6)[2],AIC(hem7)[2],AIC(hem8)[2],
         AIC(hem9)[2],AIC(hem20)[2],
         AIC(hem2)[2],  AIC(hem21)[2],AIC(hem22)[2], AIC(hem23)[2],       
         AIC(hem)[2])
q4=cbind(AIC(feet0)[2], AIC(feet4)[2],AIC(feet5)[2],AIC(feet3)[2],
         AIC(feet1)[2], AIC(feet6)[2],AIC(feet7)[2],AIC(feet8)[2],
         AIC(feet9)[2],  AIC(feet20)[2],
         AIC(feet2)[2], AIC(feet21)[2],AIC(feet22)[2],AIC(feet23)[2],
         AIC(feet)[2])
q5=cbind(AIC(edu0)[2], AIC(edu4)[2],AIC(edu5)[2],AIC(edu3)[2],
         AIC(edu1)[2], AIC(edu6)[2], AIC(edu7)[2], AIC(edu8)[2],
         AIC(edu9)[2],AIC(edu20)[2],
         AIC(edu2)[2],  AIC(edu21)[2],  AIC(edu22)[2],AIC(edu23)[2],
         AIC(edu)[2])
q6=cbind(AIC(eye0)[2],AIC(eye4)[2], AIC(eye5)[2],AIC(eye3)[2],
         AIC(eye1)[2], AIC(eye6)[2], AIC(eye7)[2], AIC(eye8)[2],
         AIC(eye9)[2],AIC(eye20)[2],
         AIC(eye2)[2],   AIC(eye21)[2],  AIC(eye22)[2], AIC(eye23)[2], 
         AIC(eye)[2])

AICs=rbind(q1,q2,q3,q4, q5, q6)
colnames(AICs)=c("Demo Only", "SES Only","Hlth Only", "Med. Exp. Only",
                 "Demo+SES", "SES+Hlth","Demo+Hlth", "Demo+Exp", 
                 "SES+Exp","Hlth+Exp",
                 "Demo/SES/Hlth","Dem/SES/Exp","Dem/Hlth/Exp","SES/Hlth/Exp",
                 "Full")
rownames(AICs)=c("BS","DOC","HEM","FEET", "EDU", "EYE")
myprint(t(AICs))

##                      BS      DOC      HEM     FEET      EDU      EYE
## Demo Only      158196.8 161170.0 171813.9 226599.8 269801.8 237408.7
## SES Only       158198.8 161272.9 164780.4 225991.0 265840.7 237076.2
## Hlth Only      156833.4 161141.7 169670.7 223338.3 271799.6 234100.6
## Med. Exp. Only 158731.2 161096.0 178049.2 229190.7 272789.7 242691.5
## Demo+SES       157614.9 161336.6 162816.0 224604.1 264409.6 234478.0
## SES+Hlth       156547.0 161317.6 158971.7 221271.0 265570.4 231621.7
## Demo+Hlth      156390.2 161221.5 163558.8 221366.4 269089.7 231462.6
## Demo+Exp       158148.0 161171.5 171409.5 226472.0 269798.9 237341.1
## SES+Exp        158143.3 161274.0 164568.2 225918.2 265843.3 237055.5
## Hlth+Exp       156752.1 161164.5 167452.3 223194.5 271240.9 232152.6
## Demo/SES/Hlth  156044.9 161386.1 156825.4 220074.3 264184.3 230006.2
## Dem/SES/Exp    157576.0 161338.0 162594.2 224519.6 264415.1 234460.8
## Dem/Hlth/Exp   156321.7 161222.1 163314.9 221310.5 269093.7 231454.5
## SES/Hlth/Exp   156466.3 161317.8 158839.2 221240.5 265571.0 231625.9
## Full           155986.3 161387.0 156684.2 220033.2 264188.7 230009.8

	BS	DOC	HEM	FEET	EDU	EYE
Demo Only	158196.8	161170.0	171813.9	226599.8	269801.8	237408.7
SES Only	158198.8	161272.9	164780.4	225991.0	265840.7	237076.2
Hlth Only	156833.4	161141.7	169670.7	223338.3	271799.6	234100.6
Med. Exp. Only	158731.2	161096.0	178049.2	229190.7	272789.7	242691.5
Demo+SES	157614.9	161336.6	162816.0	224604.1	264409.6	234478.0
SES+Hlth	156547.0	161317.6	158971.7	221271.0	265570.4	231621.7
Demo+Hlth	156390.2	161221.5	163558.8	221366.4	269089.7	231462.6
Demo+Exp	158148.0	161171.5	171409.5	226472.0	269798.9	237341.1
SES+Exp	158143.3	161274.0	164568.2	225918.2	265843.3	237055.5
Hlth+Exp	156752.1	161164.5	167452.3	223194.5	271240.9	232152.6
Demo/SES/Hlth	156044.9	161386.1	156825.4	220074.3	264184.3	230006.2
Dem/SES/Exp	157576.0	161338.0	162594.2	224519.6	264415.1	234460.8
Dem/Hlth/Exp	156321.7	161222.1	163314.9	221310.5	269093.7	231454.5
SES/Hlth/Exp	156466.3	161317.8	158839.2	221240.5	265571.0	231625.9
Full	155986.3	161387.0	156684.2	220033.2	264188.7	230009.8

Submodels Recommended by AIC

mytest=svyglm(DocDiab~MedExp,svytot,family=quasibinomial,maxit = 100)
print('Doctor Visits:', 1-mytest$deviance/mytest$null.deviance)

## [1] "Doctor Visits:"

print('Diabetes Ed:', 1-edu2$deviance/edu2$null.deviance)

## [1] "Diabetes Ed:"

print('Eye Exams: ', 1-eye2$deviance/eye2$null.deviance)

## [1] "Eye Exams: "

Model Plots

pm1=plot_model(bs, main="Blood Sugar Self Check")
pm2=plot_model(doc, main="Doctor for Diabetes")
pm3=plot_model(hem, main="Hemoglobin Checks")
pm4=plot_model(feet, main="Feet Checks")
pm5=plot_model(edu, main="Diabetes Education")
pm6=plot_model(eye, main="Eye Checks")


t1=pm1$data[,c(1,2,5,6)]
t1$Group=rep("Bld. Sugar", nrow(t1))
t2=pm2$data[,c(1,2,5,6)]
t2$Group=rep("Dr. Visit", nrow(t2))
t3=pm3$data[,c(1,2,5,6)]
t3$Group=rep("A1c Check", nrow(t3))
t4=pm4$data[,c(1,2,5,6)]
t4$Group=rep("Ft. Check", nrow(t4))
t5=pm5$data[,c(1,2,5,6)]
t5$Group=rep("Diab. Ed", nrow(t5))
t6=pm6$data[,c(1,2,5,6)]
t6$Group=rep("Eye Exam", nrow(t1))

ttot=rbind(t1,t2,t3,t4,t5,t6)
ttot$Group=as.factor(ttot$Group)

ggplot(data=ttot,
    aes(x = term,y = estimate, ymin = .5, ymax = 2.0 ))+
    geom_pointrange(aes(col=Group))+
    geom_hline(aes(fill=Group),yintercept =1, linetype=2)+
    xlab('Group')+ ylab("Odds Ratio (95% Confidence Interval)")+
    geom_errorbar(aes(ymin=conf.low,
                      ymax=conf.high,col=Group),width=0.5,cex=1)+ 
  facet_grid(~Group)+
        theme(plot.title=element_text(size=16,face="bold"),
        axis.text.y=element_text(size=10),
        axis.text.x=element_text(face="bold"),
        axis.title=element_text(size=12,face="bold"),
        strip.text.y = element_text(hjust=0,vjust = 1,angle=180,face="bold"))+
    coord_flip()

## Warning: geom_hline(): Ignoring `mapping` because `yintercept` was provided.

## Tables

a1=model_parameters(bs, df_method='wald')
a2=model_parameters(doc, df_method='wald')
a3=model_parameters(hem, df_method='wald')
a4=model_parameters(feet, df_method='wald')
a5=model_parameters(edu, df_method='wald')
a6=model_parameters(eye, df_method='wald')

myf=function(x){
x[, 5]=exp(x[,2]-qnorm(.975)*x[,3])
x[, 6]=exp(x[,2]+qnorm(.975)*x[,3])
x[, 2]=exp(x[,2])
x[,1]=x[,3]=x[,4]=x[,7]=x[8]=NULL

colnames(x)=c('Odds Ratio', 'CI Low', 'CI High','p')
return(x)
}

b1=myf(a1)
b2=myf(a2)
b3=myf(a3)
b4=myf(a4)
b5=myf(a5)
b6=myf(a6)

a=cbind(b1,b2,b3,b4,b5,b6)
rownames(a)=names(bs$coefficients)

myprint(a)

##                    Odds Ratio    CI Low   CI High
## Intercept           4.8076030 4.0892841 5.6521010
## Age 55-64           0.9983624 0.9229883 1.0798918
## Age 65+             1.0012831 0.9148452 1.0958879
## Male                0.9611329 0.9103660 1.0147310
## Black NH            1.2825174 1.1865959 1.3861928
## Hispanic            0.9681053 0.8730497 1.0735103
## Other Race          0.8985839 0.7903395 1.0216535
## Multiracial NH      1.0601528 0.8775046 1.2808183
## Unk.Race            0.9947666 0.8375464 1.1814994
## Divorced            0.7191782 0.6663072 0.7762445
## Widowed             0.7902389 0.7333444 0.8515474
## Separated           0.7404574 0.6316786 0.8679687
## Never Married       0.6923749 0.6274383 0.7640321
## Unmarried Couple    0.8803225 0.7199007 1.0764925
## Unk. Marital        0.6946510 0.4976065 0.9697221
## LT $10K             1.1227968 0.9778468 1.2892332
## LT $15K             1.1621770 1.0111991 1.3356967
## LT $20K             1.3233283 1.1689910 1.4980421
## LT $25K             1.2405090 1.1082527 1.3885484
## LT $30K             1.1740099 1.0442982 1.3198330
## LT $50K             1.0611220 0.9580109 1.1753310
## LT $75K             1.0769340 0.9758648 1.1884710
## Unk. Income         1.0183157 0.9237184 1.1226007
## Grade LTE 8         0.9701398 0.8536762 1.1024920
## Grades 9-11         0.9917350 0.8922395 1.1023255
## Grade 12            1.1476904 1.0715996 1.2291842
## 1-3 College         1.0604017 0.9903108 1.1354534
## Unk. Education      1.4130829 0.9622897 2.0750542
## Self-Employed       1.0701837 0.9493938 1.2063415
## Unemployed GTE Yr   1.0641281 0.9058003 1.2501305
## Unemployed LT Yr    1.2787771 1.0559349 1.5486473
## Homemaker           0.9832964 0.8641432 1.1188793
## Student             1.0506527 0.6618420 1.6678770
## Retired             1.1541820 1.0664359 1.2491477
## Unable to Work      1.2953508 1.1745223 1.4286094
## Unk. Employ         0.7983554 0.4898867 1.3010588
## Very Good Health    1.0364032 0.8993494 1.1943429
## Good Health         1.2903676 1.1251786 1.4798081
## Fair Health         1.4159941 1.2296536 1.6305725
## Poor Health         1.6081073 1.3812116 1.8722759
## Unk. Health Status  0.8360603 0.5490034 1.2732105
## No Health Plan      0.7839949 0.6990701 0.8792366
## Unk. Health Plan    0.7566966 0.4957571 1.1549806
## No Personal Doctor  0.6455593 0.5795589 0.7190759
## Unk. Personal Doc   0.7596508 0.5514499 1.0464582
## No Checkup          0.6615904 0.6091174 0.7185837
## Unk. Checkup        0.5382582 0.4560887 0.6352316
## Cost Impacted Care  0.8617434 0.7968444 0.9319280
## Unk Cost            0.8155038 0.5849766 1.1368769
## Medicaid Expansion  0.9710817 0.9588004 0.9835203
##                                                                                                           p
## Intercept          0.00000000000000000000000000000000000000000000000000000000000000000000000000000001431913
## Age 55-64          0.96735916305748081978066466035670600831508636474609375000000000000000000000000000000000
## Age 65+            0.97779258301950711107508595887338742613792419433593750000000000000000000000000000000000
## Male               0.15220374531122315975295578027726151049137115478515625000000000000000000000000000000000
## Black NH           0.00000000035340825844952865448199419429187173591344617307186126708984375000000000000000
## Hispanic           0.53873493279815032330759549950016662478446960449218750000000000000000000000000000000000
## Other Race         0.10250124254267696521925046226897393353283405303955078125000000000000000000000000000000
## Multiracial NH     0.54486207819687826336974012519931420683860778808593750000000000000000000000000000000000
## Unk.Race           0.95233000095697228903901532248710282146930694580078125000000000000000000000000000000000
## Divorced           0.00000000000000002659322877533184188107023837588371861784253269433975219726562500000000
## Widowed            0.00000000066171960290830152385220530586451559429406188428401947021484375000000000000000
## Separated          0.00021005248899374327092338943234750558985979296267032623291015625000000000000000000000
## Never Married      0.00000000000025556898421945935862865939558474792647757567465305328369140625000000000000
## Unmarried Couple   0.21429076025404567018561863278591772541403770446777343750000000000000000000000000000000
## Unk. Marital       0.03230735333983802970436727264313958585262298583984375000000000000000000000000000000000
## LT $10K            0.10052709202114386288773317801314988173544406890869140625000000000000000000000000000000
## LT $15K            0.03427623515942760518138143766009307000786066055297851562500000000000000000000000000000
## LT $20K            0.00000952574698560552838699083677553858251485507935285568237304687500000000000000000000
## LT $25K            0.00017907914285319309882077432050095922022592276334762573242187500000000000000000000000
## LT $30K            0.00724104240104107323150461894556428887881338596343994140625000000000000000000000000000
## LT $50K            0.25533127323688642462684583733789622783660888671875000000000000000000000000000000000000
## LT $75K            0.14046376918928071830805492936633527278900146484375000000000000000000000000000000000000
## Unk. Income        0.71521432277116392395299726558732800185680389404296875000000000000000000000000000000000
## Grade LTE 8        0.64222049407800652076616643171291798353195190429687500000000000000000000000000000000000
## Grades 9-11        0.87771971658042202690808153420221060514450073242187500000000000000000000000000000000000
## Grade 12           0.00008296685343245772449073671683805741849937476217746734619140625000000000000000000000
## 1-3 College        0.09278257629058683786382744074217043817043304443359375000000000000000000000000000000000
## Unk. Education     0.07775468725141215786678117183328140527009963989257812500000000000000000000000000000000
## Self-Employed      0.26696715468250681313477912226517219096422195434570312500000000000000000000000000000000
## Unemployed GTE Yr  0.44951099884045797594467330782208591699600219726562500000000000000000000000000000000000
## Unemployed LT Yr   0.01183411979627877266829649727242212975397706031799316406250000000000000000000000000000
## Homemaker          0.79826914595431208709896964137442409992218017578125000000000000000000000000000000000000
## Student            0.83401317003468755650885668728733435273170471191406250000000000000000000000000000000000
## Retired            0.00037896907605099720188837619794242073112400248646736145019531250000000000000000000000
## Unable to Work     0.00000022236740581648121858546196527584015711909160017967224121093750000000000000000000
## Unk. Employ        0.36611439327991346370794190079323016107082366943359375000000000000000000000000000000000
## Very Good Health   0.62124668398880578124021667463239282369613647460937500000000000000000000000000000000000
## Good Health        0.00026491225190251670943558659487848672142717987298965454101562500000000000000000000000
## Fair Health        0.00000135522979257940084994553398001215782642248086631298065185546875000000000000000000
## Poor Health        0.00000000092722374446032944329473823863452253135619685053825378417968750000000000000000
## Unk. Health Status 0.40406334393761778045472965459339320659637451171875000000000000000000000000000000000000
## No Health Plan     0.00003182191808566108259239899047621236150007462128996849060058593750000000000000000000
## Unk. Health Plan   0.19630310483579843650048246672668028622865676879882812500000000000000000000000000000000
## No Personal Doctor 0.00000000000000182580579568239027071427371673451034439494833350181579589843750000000000
## Unk. Personal Doc  0.09255241648619366712225087212573271244764328002929687500000000000000000000000000000000
## No Checkup         0.00000000000000000000011589992271340005688083640844610044950968585908412933349609375000
## Unk. Checkup       0.00000000000023292675127367875259967733292398861522087827324867248535156250000000000000
## Cost Impacted Care 0.00019558108808174224095728643835201410183799453079700469970703125000000000000000000000
## Unk Cost           0.22891304523569527828996683638251852244138717651367187500000000000000000000000000000000
## Medicaid Expansion 0.00000622046269862759041704633466984830647561466321349143981933593750000000000000000000
##                    Odds Ratio    CI Low  CI High
## Intercept           6.7305254 5.6556990 8.009615
## Age 55-64           0.9859886 0.8959963 1.085020
## Age 65+             1.0000304 0.8882060 1.125933
## Male                0.9912067 0.9401459 1.045041
## Black NH            1.0211759 0.9521023 1.095261
## Hispanic            0.9610530 0.8393964 1.100342
## Other Race          0.9115654 0.7899998 1.051838
## Multiracial NH      1.2143741 1.0183078 1.448191
## Unk.Race            0.9848283 0.8281284 1.171179
## Divorced            0.9294822 0.8551937 1.010224
## Widowed             1.0355227 0.9642852 1.112023
## Separated           1.0828293 0.9365328 1.251979
## Never Married       0.9867750 0.8934014 1.089907
## Unmarried Couple    0.9771602 0.7799468 1.224240
## Unk. Marital        1.2105476 0.8421986 1.740000
## LT $10K             1.0469885 0.9098247 1.204831
## LT $15K             1.0550724 0.9134833 1.218608
## LT $20K             1.0260433 0.9084686 1.158834
## LT $25K             1.0456553 0.9284396 1.177670
## LT $30K             1.0171972 0.9146889 1.131193
## LT $50K             1.0535857 0.9546996 1.162714
## LT $75K             1.0572047 0.9545302 1.170923
## Unk. Income         1.0397115 0.9314414 1.160567
## Grade LTE 8         1.0156157 0.8785209 1.174104
## Grades 9-11         0.9918647 0.8844966 1.112266
## Grade 12            0.9823869 0.9168739 1.052581
## 1-3 College         0.9462452 0.8818558 1.015336
## Unk. Education      0.8613904 0.5862208 1.265723
## Self-Employed       1.0591140 0.9311422 1.204674
## Unemployed GTE Yr   1.0957400 0.9149850 1.312203
## Unemployed LT Yr    0.9404354 0.7165288 1.234310
## Homemaker           0.8942227 0.7192510 1.111760
## Student             1.0600903 0.6174092 1.820173
## Retired             0.9639914 0.8861967 1.048615
## Unable to Work      0.9687772 0.8841130 1.061549
## Unk. Employ         0.8754333 0.6168014 1.242513
## Very Good Health    0.9455075 0.8096879 1.104110
## Good Health         0.9567451 0.8224553 1.112961
## Fair Health         0.9412380 0.8062717 1.098797
## Poor Health         0.9127754 0.7648041 1.089376
## Unk. Health Status  0.9362358 0.6397950 1.370028
## No Health Plan      1.0129620 0.8926615 1.149475
## Unk. Health Plan    1.2310061 0.8519059 1.778807
## No Personal Doctor  0.9273403 0.7943044 1.082658
## Unk. Personal Doc   1.1600792 0.8105714 1.660290
## No Checkup          0.9560849 0.8683007 1.052744
## Unk. Checkup        1.1447833 0.9623475 1.361804
## Cost Impacted Care  0.9243628 0.8409379 1.016064
## Unk Cost            1.3445774 0.9327876 1.938157
## Medicaid Expansion  0.9907906 0.9775016 1.004260
##                                                                                                                                 p
## Intercept          0.000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000003198505
## Age 55-64          0.772612028330227573746924463193863630294799804687500000000000000000000000000000000000000000000000000000000000
## Age 65+            0.999599359041647206325365004886407405138015747070312500000000000000000000000000000000000000000000000000000000
## Male               0.743432544998308353356719635485205799341201782226562500000000000000000000000000000000000000000000000000000000
## Black NH           0.557600903956475324463326614932157099246978759765625000000000000000000000000000000000000000000000000000000000
## Hispanic           0.565107638648636889655563209089450538158416748046875000000000000000000000000000000000000000000000000000000000
## Other Race         0.204831802793242395699380153928359504789113998413085937500000000000000000000000000000000000000000000000000000
## Multiracial NH     0.030627066080632307787379176033937255851924419403076171875000000000000000000000000000000000000000000000000000
## Unk.Race           0.862728492774807564913430724118370562791824340820312500000000000000000000000000000000000000000000000000000000
## Divorced           0.085320867796644356273461085038434248417615890502929687500000000000000000000000000000000000000000000000000000
## Widowed            0.337115676445165490804356522858142852783203125000000000000000000000000000000000000000000000000000000000000000
## Separated          0.282577778686702529853391752112656831741333007812500000000000000000000000000000000000000000000000000000000000
## Never Married      0.792941413972714781444039999769302085041999816894531250000000000000000000000000000000000000000000000000000000
## Unmarried Couple   0.840788930717374927148455299175111576914787292480468750000000000000000000000000000000000000000000000000000000
## Unk. Marital       0.301978302845645862895196387398755177855491638183593750000000000000000000000000000000000000000000000000000000
## LT $10K            0.521581444599396926165013610443565994501113891601562500000000000000000000000000000000000000000000000000000000
## LT $15K            0.465901139248613094245854426844744011759757995605468750000000000000000000000000000000000000000000000000000000
## LT $20K            0.678846276818102500882901040313299745321273803710937500000000000000000000000000000000000000000000000000000000
## LT $25K            0.461759968691711430999191634327871724963188171386718750000000000000000000000000000000000000000000000000000000
## LT $30K            0.753052670187263850642978013638639822602272033691406250000000000000000000000000000000000000000000000000000000
## LT $50K            0.299244058816078628559864682756597176194190979003906250000000000000000000000000000000000000000000000000000000
## LT $75K            0.285884023326916203444625352858565747737884521484375000000000000000000000000000000000000000000000000000000000
## Unk. Income        0.487616852114611698532087302737636491656303405761718750000000000000000000000000000000000000000000000000000000
## Grade LTE 8        0.834112352741966822655683699849760159850120544433593750000000000000000000000000000000000000000000000000000000
## Grades 9-11        0.888862815559453123270827745727729052305221557617187500000000000000000000000000000000000000000000000000000000
## Grade 12           0.613803287972615851941782239009626209735870361328125000000000000000000000000000000000000000000000000000000000
## 1-3 College        0.124371733662723388258442014375759754329919815063476562500000000000000000000000000000000000000000000000000000
## Unk. Education     0.447326389913273558818218589294701814651489257812500000000000000000000000000000000000000000000000000000000000
## Self-Employed      0.382052240690343269946538384829182177782058715820312500000000000000000000000000000000000000000000000000000000
## Unemployed GTE Yr  0.320214476936151404284913724040961824357509613037109375000000000000000000000000000000000000000000000000000000
## Unemployed LT Yr   0.658022971462196126779531368811149150133132934570312500000000000000000000000000000000000000000000000000000000
## Homemaker          0.314253736143025430571640299604041501879692077636718750000000000000000000000000000000000000000000000000000000
## Student            0.832439775608903809178684696234995499253273010253906250000000000000000000000000000000000000000000000000000000
## Retired            0.392980080060223779803152410750044509768486022949218750000000000000000000000000000000000000000000000000000000
## Unable to Work     0.496605151294632118919025742798112332820892333984375000000000000000000000000000000000000000000000000000000000
## Unk. Employ        0.456500012382724973392100764613132923841476440429687500000000000000000000000000000000000000000000000000000000
## Very Good Health   0.478818103688199991907481489761266857385635375976562500000000000000000000000000000000000000000000000000000000
## Good Health        0.566626455891365510275647920934716239571571350097656250000000000000000000000000000000000000000000000000000000
## Fair Health        0.443154206547722750997309049125760793685913085937500000000000000000000000000000000000000000000000000000000000
## Poor Health        0.311851755598924917922687427562777884304523468017578125000000000000000000000000000000000000000000000000000000
## Unk. Health Status 0.734463407349351338204712646984262391924858093261718750000000000000000000000000000000000000000000000000000000
## No Health Plan     0.841750149530809177456092129432363435626029968261718750000000000000000000000000000000000000000000000000000000
## Unk. Health Plan   0.268478494430706149387333425693213939666748046875000000000000000000000000000000000000000000000000000000000000
## No Personal Doctor 0.339696963725863554373063379898667335510253906250000000000000000000000000000000000000000000000000000000000000
## Unk. Personal Doc  0.416910433779654088226607200340367853641510009765625000000000000000000000000000000000000000000000000000000000
## No Checkup         0.360756006437789378260561079514445737004280090332031250000000000000000000000000000000000000000000000000000000
## Unk. Checkup       0.126851902016571288500657033182505983859300613403320312500000000000000000000000000000000000000000000000000000
## Cost Impacted Care 0.103156630810678093257415355310513405129313468933105468750000000000000000000000000000000000000000000000000000
## Unk Cost           0.112509710601036316823808647313853725790977478027343750000000000000000000000000000000000000000000000000000000
## Medicaid Expansion 0.179302853009032064912631199149473104625940322875976562500000000000000000000000000000000000000000000000000000
##                    Odds Ratio     CI Low    CI High
## Intercept          26.4175296 21.8585946 31.9272984
## Age 55-64           1.0149428  0.9315762  1.1057698
## Age 65+             0.6229912  0.5662265  0.6854465
## Male                0.8461374  0.8017591  0.8929720
## Black NH            0.7307308  0.6867985  0.7774733
## Hispanic            0.6381171  0.5812083  0.7005980
## Other Race          0.7086686  0.6247541  0.8038541
## Multiracial NH      0.7360778  0.6101953  0.8879298
## Unk.Race            0.6595806  0.5490457  0.7923688
## Divorced            0.7692961  0.7153946  0.8272588
## Widowed             0.6617774  0.6182305  0.7083917
## Separated           0.7162652  0.6250808  0.8207511
## Never Married       0.7109289  0.6442864  0.7844646
## Unmarried Couple    0.8402025  0.6768969  1.0429067
## Unk. Marital        0.9740428  0.6833778  1.3883380
## LT $10K             0.4656064  0.4033279  0.5375013
## LT $15K             0.5709811  0.4954387  0.6580419
## LT $20K             0.5793609  0.5074463  0.6614670
## LT $25K             0.5879599  0.5151498  0.6710609
## LT $30K             0.6425542  0.5652747  0.7303987
## LT $50K             0.7439886  0.6548214  0.8452976
## LT $75K             0.8447046  0.7355642  0.9700388
## Unk. Income         0.3929781  0.3498590  0.4414117
## Grade LTE 8         0.2835351  0.2538441  0.3166989
## Grades 9-11         0.3765687  0.3416398  0.4150687
## Grade 12            0.5859559  0.5432931  0.6319688
## 1-3 College         0.8635626  0.7953135  0.9376684
## Unk. Education      0.3288753  0.2375931  0.4552276
## Self-Employed       1.0461773  0.9053414  1.2089217
## Unemployed GTE Yr   1.0076908  0.8468238  1.1991169
## Unemployed LT Yr    1.2043004  0.9676467  1.4988316
## Homemaker           0.9618307  0.8311979  1.1129939
## Student             1.2934317  0.7790317  2.1474934
## Retired             1.0579119  0.9706178  1.1530569
## Unable to Work      0.9583523  0.8696846  1.0560600
## Unk. Employ         0.9127350  0.6953523  1.1980764
## Very Good Health    1.2198738  1.0366704  1.4354534
## Good Health         1.3426535  1.1475994  1.5708603
## Fair Health         1.3049669  1.1123875  1.5308862
## Poor Health         1.1238763  0.9510317  1.3281344
## Unk. Health Status  0.5424079  0.3950873  0.7446615
## No Health Plan      0.6631949  0.5954193  0.7386853
## Unk. Health Plan    0.4793732  0.3358660  0.6841974
## No Personal Doctor  0.4844152  0.4337420  0.5410085
## Unk. Personal Doc   0.5124193  0.3797313  0.6914721
## No Checkup          0.3691352  0.3394355  0.4014336
## Unk. Checkup        0.4340229  0.3696099  0.5096612
## Cost Impacted Care  0.8310644  0.7682554  0.8990084
## Unk Cost            0.5588881  0.4072983  0.7668971
## Medicaid Expansion  1.0482441  1.0329023  1.0638137
##                                                                                                                                                                                                                                                                                      p
## Intercept          0.00000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000007336664
## Age 55-64          0.73447732765043349800748728739563375711441040039062500000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000
## Age 65+            0.00000000000000000000028125653576118202409996060797681138865300454199314117431640625000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000
## Male               0.00000000121684239159362487321842938303717573944595642387866973876953125000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000
## Black NH           0.00000000000000000000003572508020303087798632407645627040437830146402120590209960937500000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000
## Hispanic           0.00000000000000000000431170119079938148325820135298158675141166895627975463867187500000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000
## Other Race         0.00000008542641884549786648189728666125120071228593587875366210937500000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000
## Multiracial NH     0.00136461741076221768596832273345853536739014089107513427734375000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000
## Unk.Race           0.00000872089741992858259907184237391675196704454720020294189453125000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000
## Divorced           0.00000000000148150698216156907912298412721696649896330200135707855224609375000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000
## Widowed            0.00000000000000000000000000000001415681689857560657513219082304090079560410231351852416992187500000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000
## Separated          0.00000156254854901627754119528002085814932797802612185478210449218750000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000
## Never Married      0.00000000001095100920614097043644455387045866245898650959134101867675781250000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000
## Unmarried Couple   0.11434381471533031082721265647705877199769020080566406250000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000
## Unk. Marital       0.88435885380785439480177956284023821353912353515625000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000
## LT $10K            0.00000000000000000000000017589364291784716578821018417855270854488480836153030395507812500000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000
## LT $15K            0.00000000000001000733337559038136558957343691389496598276309669017791748046875000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000
## LT $20K            0.00000000000000069564693755636690490617990434429884771816432476043701171875000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000
## LT $25K            0.00000000000000345524761310647461187012252636918674397747963666915893554687500000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000
## LT $30K            0.00000000001333796098538993537370098141359164856112329289317131042480468750000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000
## LT $50K            0.00000562245331443949978179192017435639172617811709642410278320312500000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000
## LT $75K            0.01680765530739544710847610531345708295702934265136718750000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000
## Unk. Income        0.00000000000000000000000000000000000000000000000000000007301920628676492287020133309383140840509440749883651733398437500000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000
## Grade LTE 8        0.00000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000002364421485812678025862504371801264824171084910631179809570312500000000000000000000000000000000000000000000000000000000000000000000000000000000000000
## Grades 9-11        0.00000000000000000000000000000000000000000000000000000000000000000000000000000000000005179393229289149628690597237934412078175228089094161987304687500000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000
## Grade 12           0.00000000000000000000000000000000000000000011844537335983516244692498320389972832344938069581985473632812500000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000
## 1-3 College        0.00047932480833456339499448750629539972578641027212142944335937500000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000
## Unk. Education     0.00000000002031516676047199417313657288097772379842353984713554382324218750000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000
## Self-Employed      0.54057639666792067778544605971546843647956848144531250000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000
## Unemployed GTE Yr  0.93119883310712070478842861120938323438167572021484375000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000
## Unemployed LT Yr   0.09584634118069844388454470163196674548089504241943359375000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000
## Homemaker          0.60129384805060182017655279196333140134811401367187500000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000
## Student            0.31990130900951374570695406873710453510284423828125000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000
## Retired            0.20010945781377925678512497142946813255548477172851562500000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000
## Unable to Work     0.39045111950421684809953148942440748214721679687500000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000
## Unk. Employ        0.51060971569996216334885730248061008751392364501953125000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000
## Very Good Health   0.01667942036329382246573338477446668548509478569030761718750000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000
## Good Health        0.00023428968995397976495149994224931333519634790718555450439453125000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000
## Fair Health        0.00108567256520877776677924675396980092045851051807403564453125000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000
## Poor Health        0.17047649821810237358299389143212465569376945495605468750000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000
## Unk. Health Status 0.00015478311946503457013912807838096341583877801895141601562500000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000
## No Health Plan     0.00000000000008255656818508752163750241681583474928629584610462188720703125000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000
## Unk. Health Plan   0.00005107737465298098768033685512790498250978998839855194091796875000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000
## No Personal Doctor 0.00000000000000000000000000000000000008110355115680887050887137856847175498842261731624603271484375000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000
## Unk. Personal Doc  0.00001226891186537574266910907705430133773916168138384819030761718750000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000
## No Checkup         0.00000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000848650994700296152761093138039427685725968331098556518554687500000000000000000000000000000000000000000000000000000000000000000000000000000
## Unk. Checkup       0.00000000000000000000000239042746309369703036976084753462146181846037507057189941406250000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000
## Cost Impacted Care 0.00000392908076658199136282217933668903242505621165037155151367187500000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000
## Unk Cost           0.00031342381530008759857319766695127327693626284599304199218750000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000
## Medicaid Expansion 0.00000000037738571620751176023591394459444359199551399797201156616210937500000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000
##                    Odds Ratio    CI Low   CI High
## Intercept           3.0549453 2.6601780 3.5082957
## Age 55-64           1.0723044 1.0040123 1.1452417
## Age 65+             0.9920734 0.9193072 1.0705992
## Male                1.1277590 1.0810501 1.1764860
## Black NH            1.3752980 1.3003710 1.4545422
## Hispanic            0.7357629 0.6766835 0.8000003
## Other Race          1.0238232 0.9248407 1.1333995
## Multiracial NH      0.9949511 0.8514570 1.1626280
## Unk.Race            0.9614339 0.8361246 1.1055233
## Divorced            0.8449297 0.7951678 0.8978056
## Widowed             0.8442180 0.7971632 0.8940504
## Separated           0.8938003 0.7945659 1.0054283
## Never Married       0.8950936 0.8271040 0.9686721
## Unmarried Couple    0.8585907 0.7236483 1.0186964
## Unk. Marital        0.8020380 0.6118351 1.0513697
## LT $10K             0.8385547 0.7518861 0.9352135
## LT $15K             0.9521769 0.8554718 1.0598139
## LT $20K             0.9855820 0.8940068 1.0865374
## LT $25K             0.9497414 0.8670017 1.0403772
## LT $30K             0.9394367 0.8600709 1.0261262
## LT $50K             0.9425195 0.8673866 1.0241603
## LT $75K             0.9686001 0.8932196 1.0503421
## Unk. Income         0.7391325 0.6820862 0.8009499
## Grade LTE 8         0.7177265 0.6481482 0.7947740
## Grades 9-11         0.7018309 0.6466398 0.7617326
## Grade 12            0.8888624 0.8419143 0.9384285
## 1-3 College         1.0360343 0.9804149 1.0948091
## Unk. Education      0.5122197 0.3847775 0.6818721
## Self-Employed       0.8844112 0.7981867 0.9799501
## Unemployed GTE Yr   0.8641546 0.7490373 0.9969639
## Unemployed LT Yr    1.0664054 0.8951999 1.2703538
## Homemaker           0.9286354 0.8229569 1.0478843
## Student             1.0202302 0.6765708 1.5384490
## Retired             1.1175226 1.0481033 1.1915398
## Unable to Work      1.1563547 1.0737993 1.2452571
## Unk. Employ         1.0737614 0.8493736 1.3574279
## Very Good Health    1.1108895 0.9784235 1.2612898
## Good Health         1.1988689 1.0606104 1.3551505
## Fair Health         1.2182831 1.0751875 1.3804232
## Poor Health         1.1454353 1.0009897 1.3107248
## Unk. Health Status  0.5088353 0.3772325 0.6863495
## No Health Plan      0.8277663 0.7560043 0.9063400
## Unk. Health Plan    0.9321440 0.6487282 1.3393782
## No Personal Doctor  0.5878698 0.5348488 0.6461469
## Unk. Personal Doc   0.6769646 0.4953412 0.9251827
## No Checkup          0.4633152 0.4333293 0.4953761
## Unk. Checkup        0.4697927 0.4122182 0.5354085
## Cost Impacted Care  0.7600332 0.7119165 0.8114021
## Unk Cost            0.5635641 0.3936358 0.8068485
## Medicaid Expansion  1.0211782 1.0100538 1.0324251
##                                                                                                                                           p
## Intercept          0.0000000000000000000000000000000000000000000000000000000249008551730013257788538816317469581917976029217243194580078125
## Age 55-64          0.0375976468092391533093454825120716122910380363464355468750000000000000000000000000000000000000000000000000000000000000
## Age 65+            0.8377610413758601604072850932425353676080703735351562500000000000000000000000000000000000000000000000000000000000000000
## Male               0.0000000253579028433937889944888022242253100557718425989151000976562500000000000000000000000000000000000000000000000000
## Black NH           0.0000000000000000000000000000736659277077586070823361774628779130580369383096694946289062500000000000000000000000000000
## Hispanic           0.0000000000006745592942995142982619272142841282402514480054378509521484375000000000000000000000000000000000000000000000
## Other Race         0.6499456913055233764353602055052760988473892211914062500000000000000000000000000000000000000000000000000000000000000000
## Multiracial NH     0.9492105664947871890291253293980844318866729736328125000000000000000000000000000000000000000000000000000000000000000000
## Unk.Race           0.5809565407689678551150791463442146778106689453125000000000000000000000000000000000000000000000000000000000000000000000
## Divorced           0.0000000531008414638967501724894915149377538909902796149253845214843750000000000000000000000000000000000000000000000000
## Widowed            0.0000000071630656818585928900106063466068917477969080209732055664062500000000000000000000000000000000000000000000000000
## Separated          0.0615122730454834634739036403061618329957127571105957031250000000000000000000000000000000000000000000000000000000000000
## Never Married      0.0059663074726713843584646745910049503436312079429626464843750000000000000000000000000000000000000000000000000000000000
## Unmarried Couple   0.0805291998108214890361722382294829003512859344482421875000000000000000000000000000000000000000000000000000000000000000
## Unk. Marital       0.1102096726480562283168040949021815322339534759521484375000000000000000000000000000000000000000000000000000000000000000
## LT $10K            0.0015600744184283952837449804817993026517797261476516723632812500000000000000000000000000000000000000000000000000000000
## LT $15K            0.3698182758530603742030962166609242558479309082031250000000000000000000000000000000000000000000000000000000000000000000
## LT $20K            0.7703739452412590171803685734630562365055084228515625000000000000000000000000000000000000000000000000000000000000000000
## LT $25K            0.2675143834930619468615020650759106501936912536621093750000000000000000000000000000000000000000000000000000000000000000
## LT $30K            0.1653599768033175154702263398576178587973117828369140625000000000000000000000000000000000000000000000000000000000000000
## LT $50K            0.1625018208226534643934257928776787593960762023925781250000000000000000000000000000000000000000000000000000000000000000
## LT $75K            0.4402415571516973269439176874584518373012542724609375000000000000000000000000000000000000000000000000000000000000000000
## Unk. Income        0.0000000000001636257970809874126918992409684960875893011689186096191406250000000000000000000000000000000000000000000000
## Grade LTE 8        0.0000000001833199636793728765950250991778602838166989386081695556640625000000000000000000000000000000000000000000000000
## Grades 9-11        0.0000000000000000241001248773043791470346852712225427239900454878807067871093750000000000000000000000000000000000000000
## Grade 12           0.0000208887013360740147788381648741307117234100587666034698486328125000000000000000000000000000000000000000000000000000
## 1-3 College        0.2086085674119726174335909263390931300818920135498046875000000000000000000000000000000000000000000000000000000000000000
## Unk. Education     0.0000045803084349677130144781744824911129398969933390617370605468750000000000000000000000000000000000000000000000000000
## Self-Employed      0.0189288942902333216788957059861786547116935253143310546875000000000000000000000000000000000000000000000000000000000000
## Unemployed GTE Yr  0.0453235193238187145103879061025509145110845565795898437500000000000000000000000000000000000000000000000000000000000000
## Unemployed LT Yr   0.4714842422111714670407423000142443925142288208007812500000000000000000000000000000000000000000000000000000000000000000
## Homemaker          0.2296933043069475099962062358827097341418266296386718750000000000000000000000000000000000000000000000000000000000000000
## Student            0.9238627371474560368014294908789452165365219116210937500000000000000000000000000000000000000000000000000000000000000000
## Retired            0.0006844088261441343959065597424284987937426194548606872558593750000000000000000000000000000000000000000000000000000000
## Unable to Work     0.0001210401481981040095088630881292601770837791264057159423828125000000000000000000000000000000000000000000000000000000
## Unk. Employ        0.5518324979780881989199770032428205013275146484375000000000000000000000000000000000000000000000000000000000000000000000
## Very Good Health   0.1045345321649019248999579190240183379501104354858398437500000000000000000000000000000000000000000000000000000000000000
## Good Health        0.0037177620328593965169650115143440416431985795497894287109375000000000000000000000000000000000000000000000000000000000
## Fair Health        0.0019542058027247361656764113035933405626565217971801757812500000000000000000000000000000000000000000000000000000000000
## Poor Health        0.0483435677288227422243593878192768897861242294311523437500000000000000000000000000000000000000000000000000000000000000
## Unk. Health Status 0.0000096526973837531082830998307997560914373025298118591308593750000000000000000000000000000000000000000000000000000000
## No Health Plan     0.0000440134025718723765740331144158403731125872582197189331054687500000000000000000000000000000000000000000000000000000
## Unk. Health Plan   0.7039810916440198962362728707375936210155487060546875000000000000000000000000000000000000000000000000000000000000000000
## No Personal Doctor 0.0000000000000000000000000003262864141145576026654603896126616291439859196543693542480468750000000000000000000000000000
## Unk. Personal Doc  0.0143703202295190034243344001652076258324086666107177734375000000000000000000000000000000000000000000000000000000000000
## No Checkup         0.0000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000002482134
## Unk. Checkup       0.0000000000000000000000000000100046652041473413299443179225178823799069505184888839721679687500000000000000000000000000
## Cost Impacted Care 0.0000000000000001994847199035259128260116900932530370482709258794784545898437500000000000000000000000000000000000000000
## Unk Cost           0.0017355237780292474651655521000748194637708365917205810546875000000000000000000000000000000000000000000000000000000000
## Medicaid Expansion 0.0001769040174416382476662440437209511401306372135877609252929687500000000000000000000000000000000000000000000000000000
##                    Odds Ratio    CI Low   CI High
## Intercept           1.9148166 1.6803052 2.1820576
## Age 55-64           0.9848470 0.9289930 1.0440591
## Age 65+             0.8053338 0.7544788 0.8596165
## Male                0.8200592 0.7902788 0.8509618
## Black NH            1.2047456 1.1462959 1.2661758
## Hispanic            0.8024972 0.7410621 0.8690254
## Other Race          0.7764074 0.7054408 0.8545131
## Multiracial NH      1.1487316 1.0086623 1.3082518
## Unk.Race            0.9383535 0.8178328 1.0766349
## Divorced            0.9396976 0.8904366 0.9916837
## Widowed             0.8207941 0.7812936 0.8622916
## Separated           0.8720689 0.7820683 0.9724268
## Never Married       0.8985689 0.8377765 0.9637726
## Unmarried Couple    0.9791997 0.8384951 1.1435154
## Unk. Marital        0.9644829 0.7692128 1.2093238
## LT $10K             0.7085262 0.6417572 0.7822419
## LT $15K             0.7843913 0.7146975 0.8608813
## LT $20K             0.8297616 0.7627364 0.9026767
## LT $25K             0.8490724 0.7846254 0.9188129
## LT $30K             0.9017922 0.8353957 0.9734658
## LT $50K             0.9385950 0.8745959 1.0072773
## LT $75K             0.9451581 0.8811170 1.0138539
## Unk. Income         0.7646606 0.7133066 0.8197119
## Grade LTE 8         0.4152013 0.3768576 0.4574463
## Grades 9-11         0.5065922 0.4702807 0.5457073
## Grade 12            0.7109757 0.6779143 0.7456495
## 1-3 College         1.0272219 0.9788085 1.0780298
## Unk. Education      0.5768030 0.4413774 0.7537807
## Self-Employed       0.8851956 0.8089722 0.9686011
## Unemployed GTE Yr   0.9562881 0.8509815 1.0746260
## Unemployed LT Yr    1.1241592 0.9546080 1.3238249
## Homemaker           0.9589809 0.8624308 1.0663399
## Student             1.0701139 0.7306768 1.5672371
## Retired             1.1949974 1.1301378 1.2635793
## Unable to Work      1.1580792 1.0839942 1.2372276
## Unk. Employ         1.0770257 0.8360078 1.3875281
## Very Good Health    1.0435402 0.9258028 1.1762506
## Good Health         1.0673230 0.9501820 1.1989054
## Fair Health         1.0863658 0.9649577 1.2230490
## Poor Health         1.1250510 0.9916971 1.2763370
## Unk. Health Status  1.0735583 0.7063243 1.6317257
## No Health Plan      0.9154596 0.8392441 0.9985966
## Unk. Health Plan    0.6444596 0.4648099 0.8935443
## No Personal Doctor  0.8178738 0.7469890 0.8954851
## Unk. Personal Doc   0.7963063 0.5797205 1.0938094
## No Checkup          0.8948622 0.8347070 0.9593526
## Unk. Checkup        0.7053637 0.6221793 0.7996697
## Cost Impacted Care  1.0127611 0.9530306 1.0762351
## Unk Cost            0.8848785 0.6597807 1.1867729
## Medicaid Expansion  0.9963865 0.9873239 1.0055323
##                                                                                                   p
## Intercept          0.000000000000000000000194611495821020182000332665595010439574252814054489135742
## Age 55-64          0.608249082848995481498377557727508246898651123046875000000000000000000000000000
## Age 65+            0.000000000077778222721213628519480731693391817316296510398387908935546875000000
## Male               0.000000000000000000000000077868103921608809643052639959392990931519307196140289
## Black NH           0.000000000000213019225806182692308128612257434042476234026253223419189453125000
## Hispanic           0.000000061470902873012821061513011322574584482936188578605651855468750000000000
## Other Race         0.000000228416359114418617373178099771990900990203954279422760009765625000000000
## Multiracial NH     0.036621977640462592684666986997399362735450267791748046875000000000000000000000
## Unk.Race           0.364310216949613296932852790632750838994979858398437500000000000000000000000000
## Divorced           0.023579061611524440578158134940167656168341636657714843750000000000000000000000
## Widowed            0.000000000000004255979610553961257294469655576563127397093921899795532226562500
## Separated          0.013776081980628135034705472605764953186735510826110839843750000000000000000000
## Never Married      0.002768482990417205340943906932693607814144343137741088867187500000000000000000
## Unmarried Couple   0.790566613487386771197407142608426511287689208984375000000000000000000000000000
## Unk. Marital       0.754045130589864442960390533698955550789833068847656250000000000000000000000000
## LT $10K            0.000000000008926294514695059166727031385590862555545754730701446533203125000000
## LT $15K            0.000000313510582436308682602668640448939640918979421257972717285156250000000000
## LT $20K            0.000014085345378567540580022621199418608739506453275680541992187500000000000000
## LT $25K            0.000048605557390022070219856575867822812142549082636833190917968750000000000000
## LT $30K            0.008069892001804883016613523238902416778728365898132324218750000000000000000000
## LT $50K            0.078624975130611549078984978677908657118678092956542968750000000000000000000000
## LT $75K            0.115116260958737379183070004273758968338370323181152343750000000000000000000000
## Unk. Income        0.000000000000039033146848960911037829113467978459084406495094299316406250000000
## Grade LTE 8        0.000000000000000000000000000000000000000000000000000000000000000000000113968806
## Grades 9-11        0.000000000000000000000000000000000000000000000000000000000000000000000009194764
## Grade 12           0.000000000000000000000000000000000000000000009210162620845256087852293003237492
## 1-3 College        0.275546016685787376054861397278727963566780090332031250000000000000000000000000
## Unk. Education     0.000055753362802607298975669791740727987416903488337993621826171875000000000000
## Self-Employed      0.007946130234102928915906893791998300002887845039367675781250000000000000000000
## Unemployed GTE Yr  0.452733778960951793735034698329400271177291870117187500000000000000000000000000
## Unemployed LT Yr   0.160602217118000739626282324934436473995447158813476562500000000000000000000000
## Homemaker          0.439169539215032322942988685099408030509948730468750000000000000000000000000000
## Student            0.727765573509650653960534327779896557331085205078125000000000000000000000000000
## Retired            0.000000000393753008596557683637651159358483710093423724174499511718750000000000
## Unable to Work     0.000013554316417285498090096462764364559916430152952671051025390625000000000000
## Unk. Employ        0.565889051110397334198864882637280970811843872070312500000000000000000000000000
## Very Good Health   0.485324261298263337494063307531177997589111328125000000000000000000000000000000
## Good Health        0.272016328246508953814242204316542483866214752197265625000000000000000000000000
## Fair Health        0.170682671027813898101399558981938753277063369750976562500000000000000000000000
## Poor Health        0.067185256727984227209660161861393135040998458862304687500000000000000000000000
## Unk. Health Status 0.739671872898193694112478624447248876094818115234375000000000000000000000000000
## No Health Plan     0.046412848233315186219360981567660928703844547271728515625000000000000000000000
## Unk. Health Plan   0.008412703267078136190604276123394811293110251426696777343750000000000000000000
## No Personal Doctor 0.000013837587814312536022617189379602109511324670165777206420898437500000000000
## Unk. Personal Doc  0.159626410122589462714515207153453957289457321166992187500000000000000000000000
## No Checkup         0.001755980669390479111807712442328011093195527791976928710937500000000000000000
## Unk. Checkup       0.000000049941603403678036191687406031292084662709385156631469726562500000000000
## Cost Impacted Care 0.682655627341932857987671923183370381593704223632812500000000000000000000000000
## Unk Cost           0.414145668686042744965902784315403550863265991210937500000000000000000000000000
## Medicaid Expansion 0.437440576685281135027594245912041515111923217773437500000000000000000000000000
##                    Odds Ratio    CI Low   CI High
## Intercept           3.1349921 2.7097418 3.6269786
## Age 55-64           1.1901568 1.1176350 1.2673845
## Age 65+             1.7040304 1.5809250 1.8367218
## Male                0.9323202 0.8943690 0.9718819
## Black NH            1.3368112 1.2648845 1.4128279
## Hispanic            1.1028775 1.0083700 1.2062424
## Other Race          1.3141434 1.1849290 1.4574483
## Multiracial NH      0.9358732 0.8111642 1.0797551
## Unk.Race            0.9910834 0.8616821 1.1399172
## Divorced            0.8694887 0.8194556 0.9225765
## Widowed             0.8963491 0.8469256 0.9486567
## Separated           0.7638878 0.6843567 0.8526614
## Never Married       1.0161569 0.9418854 1.0962851
## Unmarried Couple    0.8345826 0.7041996 0.9891061
## Unk. Marital        0.8998567 0.6885621 1.1759898
## LT $10K             0.7120218 0.6414284 0.7903845
## LT $15K             0.7072916 0.6371279 0.7851821
## LT $20K             0.7034109 0.6395395 0.7736611
## LT $25K             0.7124534 0.6526415 0.7777469
## LT $30K             0.8029180 0.7377618 0.8738286
## LT $50K             0.7891299 0.7277890 0.8556409
## LT $75K             0.9187237 0.8477912 0.9955910
## Unk. Income         0.7623985 0.7029600 0.8268628
## Grade LTE 8         0.6847325 0.6164036 0.7606356
## Grades 9-11         0.6327026 0.5814374 0.6884879
## Grade 12            0.8073128 0.7644827 0.8525425
## 1-3 College         0.8721372 0.8249653 0.9220064
## Unk. Education      0.6369679 0.4784287 0.8480429
## Self-Employed       0.8722099 0.7900104 0.9629622
## Unemployed GTE Yr   1.0230755 0.8977290 1.1659237
## Unemployed LT Yr    1.0355014 0.8601075 1.2466617
## Homemaker           0.9782314 0.8640637 1.1074840
## Student             1.0841692 0.7050717 1.6670971
## Retired             1.2402428 1.1633884 1.3221743
## Unable to Work      1.1514563 1.0722237 1.2365440
## Unk. Employ         1.0608033 0.7831039 1.4369787
## Very Good Health    0.9769805 0.8543948 1.1171544
## Good Health         0.9286305 0.8156700 1.0572348
## Fair Health         0.8767030 0.7686827 0.9999029
## Poor Health         0.8271038 0.7193228 0.9510343
## Unk. Health Status  0.6720790 0.4652959 0.9707590
## No Health Plan      0.6248344 0.5712154 0.6834864
## Unk. Health Plan    1.0753962 0.7702044 1.5015196
## No Personal Doctor  0.7699015 0.6957930 0.8519034
## Unk. Personal Doc   0.6324627 0.4596955 0.8701610
## No Checkup          0.4776366 0.4466665 0.5107540
## Unk. Checkup        0.6260178 0.5492241 0.7135491
## Cost Impacted Care  0.7186076 0.6751759 0.7648330
## Unk Cost            1.0333878 0.7680486 1.3903943
## Medicaid Expansion  1.0049805 0.9946633 1.0154048
##                                                                                                                                  p
## Intercept          0.0000000000000000000000000000000000000000000000000000309757504447520276048833043347485727281309664249420166016
## Age 55-64          0.0000000573517849910442583782205216991201268683653324842453002929687500000000000000000000000000000000000000000
## Age 65+            0.0000000000000000000000000000000000000000000427458615111643256030682369583928448264487087726593017578125000000
## Male               0.0009496546426072406113169654950922904390608891844749450683593750000000000000000000000000000000000000000000000
## Black NH           0.0000000000000000000000008146924278552254079441286016560752614168450236320495605468750000000000000000000000000
## Hispanic           0.0321689767970365636529095354489982128143310546875000000000000000000000000000000000000000000000000000000000000
## Other Race         0.0000002303796490388857468988720311742213198158424347639083862304687500000000000000000000000000000000000000000
## Multiracial NH     0.3637146964338123789062251489667687565088272094726562500000000000000000000000000000000000000000000000000000000
## Unk.Race           0.9001525085933378411695571230666246265172958374023437500000000000000000000000000000000000000000000000000000000
## Divorced           0.0000037481379895243847652063634567909389261330943554639816284179687500000000000000000000000000000000000000000
## Widowed            0.0001559942890354425908735341854693956520350184291601181030273437500000000000000000000000000000000000000000000
## Separated          0.0000015758047305354722273946360688867684984870720654726028442382812500000000000000000000000000000000000000000
## Never Married      0.6789563947715180347586283460259437561035156250000000000000000000000000000000000000000000000000000000000000000
## Unmarried Couple   0.0369483590882716539383068266033660620450973510742187500000000000000000000000000000000000000000000000000000000
## Unk. Marital       0.4396624775595596723576363729080185294151306152343750000000000000000000000000000000000000000000000000000000000
## LT $10K            0.0000000001824759450226497865463204384184336959151551127433776855468750000000000000000000000000000000000000000
## LT $15K            0.0000000000821102072495479035768584097354505502153187990188598632812500000000000000000000000000000000000000000
## LT $20K            0.0000000000004382349531736601449399826080366437963675707578659057617187500000000000000000000000000000000000000
## LT $25K            0.0000000000000351740834595746430313659147692817441566148772835731506347656250000000000000000000000000000000000
## LT $30K            0.0000003710321689380517566929015838006478134047938510775566101074218750000000000000000000000000000000000000000
## LT $50K            0.0000000096989396208706389994336216631154456990770995616912841796875000000000000000000000000000000000000000000
## LT $75K            0.0386649434287228144224712877985439263284206390380859375000000000000000000000000000000000000000000000000000000
## Unk. Income        0.0000000000574252809521420492112203648460422300559002906084060668945312500000000000000000000000000000000000000
## Grade LTE 8        0.0000000000016586321898435022585433645803121294193260837346315383911132812500000000000000000000000000000000000
## Grades 9-11        0.0000000000000000000000000249783316700406036038432933121811174714821390807628631591796875000000000000000000000
## Grade 12           0.0000000000000141083905492075170140867101942205863451818004250526428222656250000000000000000000000000000000000
## 1-3 College        0.0000014208843129762269386247297564551672621746547520160675048828125000000000000000000000000000000000000000000
## Unk. Education     0.0020108011975725364507694425952877281815744936466217041015625000000000000000000000000000000000000000000000000
## Self-Employed      0.0067844821133909594818023336415535595733672380447387695312500000000000000000000000000000000000000000000000000
## Unemployed GTE Yr  0.7322718631935491995221809702343307435512542724609375000000000000000000000000000000000000000000000000000000000
## Unemployed LT Yr   0.7125523589270206237245020020054653286933898925781250000000000000000000000000000000000000000000000000000000000
## Homemaker          0.7281418305675032565105198045785073190927505493164062500000000000000000000000000000000000000000000000000000000
## Student            0.7127813267991249812638443472678773105144500732421875000000000000000000000000000000000000000000000000000000000
## Retired            0.0000000000421352699990116928881189739541923700016923248767852783203125000000000000000000000000000000000000000
## Unable to Work     0.0001057443199712882711972100624464587781403679400682449340820312500000000000000000000000000000000000000000000
## Unk. Employ        0.7030815211195020619783235815702937543392181396484375000000000000000000000000000000000000000000000000000000000
## Very Good Health   0.7335205379213609599986511966562829911708831787109375000000000000000000000000000000000000000000000000000000000
## Good Health        0.2631786275194775148733583591820206493139266967773437500000000000000000000000000000000000000000000000000000000
## Fair Health        0.0498323573866237962137759609504428226500749588012695312500000000000000000000000000000000000000000000000000000
## Poor Health        0.0077054438617093256105872178807203454198315739631652832031250000000000000000000000000000000000000000000000000
## Unk. Health Status 0.0341634060822231730436548957641207380220293998718261718750000000000000000000000000000000000000000000000000000
## No Health Plan     0.0000000000000000000000009435012358024895320202096726802665216382592916488647460937500000000000000000000000000
## Unk. Health Plan   0.6695098064686197947281698361621238291263580322265625000000000000000000000000000000000000000000000000000000000
## No Personal Doctor 0.0000004111823032434426651061942781240077238180674612522125244140625000000000000000000000000000000000000000000
## Unk. Personal Doc  0.0048886139412821659130936602366546139819547533988952636718750000000000000000000000000000000000000000000000000
## No Checkup         0.0000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000002166653
## Unk. Checkup       0.0000000000023149365465357339267764291701467982420581392943859100341796875000000000000000000000000000000000000
## Cost Impacted Care 0.0000000000000000000000002831779899184108678904941980292164771526586264371871948242187500000000000000000000000
## Unk Cost           0.8282698952605545006377951722242869436740875244140625000000000000000000000000000000000000000000000000000000000
## Medicaid Expansion 0.3453595283850243724543815915239974856376647949218750000000000000000000000000000000000000000000000000000000000

	Odds Ratio	CI Low	CI High	p	Odds Ratio	CI Low	CI High	p	Odds Ratio	CI Low	CI High	p	Odds Ratio	CI Low	CI High	p	Odds Ratio	CI Low	CI High	p	Odds Ratio	CI Low	CI High	p
Intercept	4.8076030	4.0892841	5.6521010	0.0000000	6.7305254	5.6556990	8.009615	0.0000000	26.4175296	21.8585946	31.9272984	0.0000000	3.0549453	2.6601780	3.5082957	0.0000000	1.9148166	1.6803052	2.1820576	0.0000000	3.1349921	2.7097418	3.6269786	0.0000000
Age 55-64	0.9983624	0.9229883	1.0798918	0.9673592	0.9859886	0.8959963	1.085020	0.7726120	1.0149428	0.9315762	1.1057698	0.7344773	1.0723044	1.0040123	1.1452417	0.0375976	0.9848470	0.9289930	1.0440591	0.6082491	1.1901568	1.1176350	1.2673845	0.0000001
Age 65+	1.0012831	0.9148452	1.0958879	0.9777926	1.0000304	0.8882060	1.125933	0.9995994	0.6229912	0.5662265	0.6854465	0.0000000	0.9920734	0.9193072	1.0705992	0.8377610	0.8053338	0.7544788	0.8596165	0.0000000	1.7040304	1.5809250	1.8367218	0.0000000
Male	0.9611329	0.9103660	1.0147310	0.1522037	0.9912067	0.9401459	1.045041	0.7434325	0.8461374	0.8017591	0.8929720	0.0000000	1.1277590	1.0810501	1.1764860	0.0000000	0.8200592	0.7902788	0.8509618	0.0000000	0.9323202	0.8943690	0.9718819	0.0009497
Black NH	1.2825174	1.1865959	1.3861928	0.0000000	1.0211759	0.9521023	1.095261	0.5576009	0.7307308	0.6867985	0.7774733	0.0000000	1.3752980	1.3003710	1.4545422	0.0000000	1.2047456	1.1462959	1.2661758	0.0000000	1.3368112	1.2648845	1.4128279	0.0000000
Hispanic	0.9681053	0.8730497	1.0735103	0.5387349	0.9610530	0.8393964	1.100342	0.5651076	0.6381171	0.5812083	0.7005980	0.0000000	0.7357629	0.6766835	0.8000003	0.0000000	0.8024972	0.7410621	0.8690254	0.0000001	1.1028775	1.0083700	1.2062424	0.0321690
Other Race	0.8985839	0.7903395	1.0216535	0.1025012	0.9115654	0.7899998	1.051838	0.2048318	0.7086686	0.6247541	0.8038541	0.0000001	1.0238232	0.9248407	1.1333995	0.6499457	0.7764074	0.7054408	0.8545131	0.0000002	1.3141434	1.1849290	1.4574483	0.0000002
Multiracial NH	1.0601528	0.8775046	1.2808183	0.5448621	1.2143741	1.0183078	1.448191	0.0306271	0.7360778	0.6101953	0.8879298	0.0013646	0.9949511	0.8514570	1.1626280	0.9492106	1.1487316	1.0086623	1.3082518	0.0366220	0.9358732	0.8111642	1.0797551	0.3637147
Unk.Race	0.9947666	0.8375464	1.1814994	0.9523300	0.9848283	0.8281284	1.171179	0.8627285	0.6595806	0.5490457	0.7923688	0.0000087	0.9614339	0.8361246	1.1055233	0.5809565	0.9383535	0.8178328	1.0766349	0.3643102	0.9910834	0.8616821	1.1399172	0.9001525
Divorced	0.7191782	0.6663072	0.7762445	0.0000000	0.9294822	0.8551937	1.010224	0.0853209	0.7692961	0.7153946	0.8272588	0.0000000	0.8449297	0.7951678	0.8978056	0.0000001	0.9396976	0.8904366	0.9916837	0.0235791	0.8694887	0.8194556	0.9225765	0.0000037
Widowed	0.7902389	0.7333444	0.8515474	0.0000000	1.0355227	0.9642852	1.112023	0.3371157	0.6617774	0.6182305	0.7083917	0.0000000	0.8442180	0.7971632	0.8940504	0.0000000	0.8207941	0.7812936	0.8622916	0.0000000	0.8963491	0.8469256	0.9486567	0.0001560
Separated	0.7404574	0.6316786	0.8679687	0.0002101	1.0828293	0.9365328	1.251979	0.2825778	0.7162652	0.6250808	0.8207511	0.0000016	0.8938003	0.7945659	1.0054283	0.0615123	0.8720689	0.7820683	0.9724268	0.0137761	0.7638878	0.6843567	0.8526614	0.0000016
Never Married	0.6923749	0.6274383	0.7640321	0.0000000	0.9867750	0.8934014	1.089908	0.7929414	0.7109289	0.6442864	0.7844646	0.0000000	0.8950936	0.8271040	0.9686721	0.0059663	0.8985689	0.8377765	0.9637726	0.0027685	1.0161569	0.9418854	1.0962851	0.6789564
Unmarried Couple	0.8803225	0.7199007	1.0764925	0.2142908	0.9771602	0.7799468	1.224240	0.8407889	0.8402025	0.6768969	1.0429067	0.1143438	0.8585907	0.7236483	1.0186964	0.0805292	0.9791997	0.8384951	1.1435154	0.7905666	0.8345826	0.7041996	0.9891061	0.0369484
Unk. Marital	0.6946510	0.4976065	0.9697221	0.0323074	1.2105476	0.8421986	1.740000	0.3019783	0.9740428	0.6833778	1.3883380	0.8843589	0.8020380	0.6118351	1.0513697	0.1102097	0.9644829	0.7692128	1.2093238	0.7540451	0.8998567	0.6885621	1.1759898	0.4396625
LT $10K	1.1227968	0.9778468	1.2892332	0.1005271	1.0469885	0.9098247	1.204831	0.5215814	0.4656064	0.4033279	0.5375013	0.0000000	0.8385547	0.7518861	0.9352135	0.0015601	0.7085262	0.6417572	0.7822419	0.0000000	0.7120218	0.6414284	0.7903845	0.0000000
LT $15K	1.1621770	1.0111991	1.3356967	0.0342762	1.0550724	0.9134833	1.218608	0.4659011	0.5709811	0.4954387	0.6580419	0.0000000	0.9521769	0.8554718	1.0598139	0.3698183	0.7843913	0.7146975	0.8608813	0.0000003	0.7072916	0.6371279	0.7851821	0.0000000
LT $20K	1.3233283	1.1689910	1.4980421	0.0000095	1.0260433	0.9084686	1.158834	0.6788463	0.5793609	0.5074463	0.6614670	0.0000000	0.9855820	0.8940068	1.0865374	0.7703739	0.8297616	0.7627364	0.9026767	0.0000141	0.7034109	0.6395395	0.7736611	0.0000000
LT $25K	1.2405090	1.1082527	1.3885484	0.0001791	1.0456553	0.9284396	1.177670	0.4617600	0.5879599	0.5151498	0.6710609	0.0000000	0.9497414	0.8670017	1.0403772	0.2675144	0.8490724	0.7846254	0.9188129	0.0000486	0.7124534	0.6526415	0.7777469	0.0000000
LT $30K	1.1740099	1.0442982	1.3198330	0.0072410	1.0171972	0.9146889	1.131193	0.7530527	0.6425542	0.5652747	0.7303987	0.0000000	0.9394367	0.8600709	1.0261262	0.1653600	0.9017922	0.8353957	0.9734658	0.0080699	0.8029180	0.7377618	0.8738286	0.0000004
LT $50K	1.0611220	0.9580109	1.1753310	0.2553313	1.0535857	0.9546996	1.162714	0.2992441	0.7439886	0.6548214	0.8452976	0.0000056	0.9425195	0.8673866	1.0241603	0.1625018	0.9385950	0.8745959	1.0072773	0.0786250	0.7891299	0.7277890	0.8556409	0.0000000
LT $75K	1.0769340	0.9758648	1.1884710	0.1404638	1.0572047	0.9545302	1.170923	0.2858840	0.8447046	0.7355642	0.9700388	0.0168077	0.9686001	0.8932196	1.0503421	0.4402416	0.9451581	0.8811170	1.0138539	0.1151163	0.9187237	0.8477912	0.9955910	0.0386649
Unk. Income	1.0183157	0.9237184	1.1226007	0.7152143	1.0397115	0.9314414	1.160567	0.4876169	0.3929781	0.3498590	0.4414117	0.0000000	0.7391325	0.6820862	0.8009499	0.0000000	0.7646606	0.7133066	0.8197119	0.0000000	0.7623985	0.7029600	0.8268628	0.0000000
Grade LTE 8	0.9701398	0.8536762	1.1024920	0.6422205	1.0156157	0.8785209	1.174104	0.8341124	0.2835351	0.2538441	0.3166989	0.0000000	0.7177265	0.6481482	0.7947740	0.0000000	0.4152013	0.3768576	0.4574463	0.0000000	0.6847325	0.6164036	0.7606356	0.0000000
Grades 9-11	0.9917350	0.8922395	1.1023255	0.8777197	0.9918647	0.8844966	1.112266	0.8888628	0.3765687	0.3416398	0.4150687	0.0000000	0.7018309	0.6466398	0.7617326	0.0000000	0.5065922	0.4702807	0.5457073	0.0000000	0.6327026	0.5814374	0.6884879	0.0000000
Grade 12	1.1476904	1.0715996	1.2291842	0.0000830	0.9823869	0.9168739	1.052581	0.6138033	0.5859559	0.5432931	0.6319688	0.0000000	0.8888624	0.8419143	0.9384285	0.0000209	0.7109757	0.6779143	0.7456495	0.0000000	0.8073128	0.7644827	0.8525425	0.0000000
1-3 College	1.0604017	0.9903108	1.1354534	0.0927826	0.9462452	0.8818558	1.015336	0.1243717	0.8635626	0.7953135	0.9376684	0.0004793	1.0360343	0.9804149	1.0948091	0.2086086	1.0272219	0.9788085	1.0780298	0.2755460	0.8721372	0.8249653	0.9220064	0.0000014
Unk. Education	1.4130829	0.9622897	2.0750542	0.0777547	0.8613904	0.5862208	1.265723	0.4473264	0.3288753	0.2375931	0.4552276	0.0000000	0.5122197	0.3847775	0.6818721	0.0000046	0.5768030	0.4413774	0.7537807	0.0000558	0.6369679	0.4784287	0.8480429	0.0020108
Self-Employed	1.0701837	0.9493938	1.2063415	0.2669672	1.0591140	0.9311422	1.204674	0.3820522	1.0461773	0.9053414	1.2089217	0.5405764	0.8844112	0.7981867	0.9799501	0.0189289	0.8851956	0.8089722	0.9686011	0.0079461	0.8722099	0.7900104	0.9629622	0.0067845
Unemployed GTE Yr	1.0641281	0.9058003	1.2501305	0.4495110	1.0957400	0.9149850	1.312203	0.3202145	1.0076908	0.8468238	1.1991169	0.9311988	0.8641546	0.7490373	0.9969639	0.0453235	0.9562881	0.8509815	1.0746260	0.4527338	1.0230755	0.8977290	1.1659237	0.7322719
Unemployed LT Yr	1.2787771	1.0559349	1.5486473	0.0118341	0.9404354	0.7165288	1.234310	0.6580230	1.2043004	0.9676467	1.4988316	0.0958463	1.0664054	0.8951999	1.2703538	0.4714842	1.1241592	0.9546080	1.3238249	0.1606022	1.0355014	0.8601075	1.2466617	0.7125524
Homemaker	0.9832964	0.8641432	1.1188793	0.7982691	0.8942227	0.7192510	1.111760	0.3142537	0.9618307	0.8311979	1.1129939	0.6012938	0.9286354	0.8229569	1.0478843	0.2296933	0.9589809	0.8624308	1.0663399	0.4391695	0.9782314	0.8640637	1.1074840	0.7281418
Student	1.0506527	0.6618420	1.6678770	0.8340132	1.0600903	0.6174092	1.820173	0.8324398	1.2934317	0.7790317	2.1474934	0.3199013	1.0202302	0.6765708	1.5384490	0.9238627	1.0701139	0.7306768	1.5672371	0.7277656	1.0841692	0.7050717	1.6670971	0.7127813
Retired	1.1541820	1.0664359	1.2491477	0.0003790	0.9639914	0.8861967	1.048615	0.3929801	1.0579119	0.9706178	1.1530569	0.2001095	1.1175226	1.0481033	1.1915398	0.0006844	1.1949974	1.1301378	1.2635793	0.0000000	1.2402428	1.1633884	1.3221743	0.0000000
Unable to Work	1.2953508	1.1745223	1.4286094	0.0000002	0.9687772	0.8841130	1.061549	0.4966052	0.9583523	0.8696846	1.0560600	0.3904511	1.1563547	1.0737993	1.2452571	0.0001210	1.1580792	1.0839942	1.2372276	0.0000136	1.1514563	1.0722237	1.2365440	0.0001057
Unk. Employ	0.7983554	0.4898867	1.3010588	0.3661144	0.8754333	0.6168014	1.242513	0.4565000	0.9127350	0.6953523	1.1980764	0.5106097	1.0737614	0.8493736	1.3574279	0.5518325	1.0770257	0.8360078	1.3875281	0.5658891	1.0608033	0.7831039	1.4369787	0.7030815
Very Good Health	1.0364032	0.8993494	1.1943429	0.6212467	0.9455075	0.8096879	1.104110	0.4788181	1.2198738	1.0366704	1.4354534	0.0166794	1.1108895	0.9784235	1.2612898	0.1045345	1.0435402	0.9258028	1.1762506	0.4853243	0.9769805	0.8543948	1.1171544	0.7335205
Good Health	1.2903676	1.1251786	1.4798081	0.0002649	0.9567451	0.8224553	1.112961	0.5666265	1.3426535	1.1475994	1.5708603	0.0002343	1.1988689	1.0606104	1.3551505	0.0037178	1.0673230	0.9501820	1.1989054	0.2720163	0.9286305	0.8156700	1.0572348	0.2631786
Fair Health	1.4159941	1.2296536	1.6305725	0.0000014	0.9412380	0.8062717	1.098797	0.4431542	1.3049669	1.1123875	1.5308862	0.0010857	1.2182831	1.0751875	1.3804232	0.0019542	1.0863658	0.9649577	1.2230490	0.1706827	0.8767030	0.7686827	0.9999029	0.0498324
Poor Health	1.6081073	1.3812116	1.8722759	0.0000000	0.9127754	0.7648041	1.089376	0.3118518	1.1238763	0.9510317	1.3281344	0.1704765	1.1454353	1.0009897	1.3107248	0.0483436	1.1250510	0.9916971	1.2763370	0.0671853	0.8271038	0.7193228	0.9510343	0.0077054
Unk. Health Status	0.8360603	0.5490034	1.2732105	0.4040633	0.9362358	0.6397950	1.370028	0.7344634	0.5424079	0.3950873	0.7446615	0.0001548	0.5088353	0.3772325	0.6863495	0.0000097	1.0735583	0.7063243	1.6317257	0.7396719	0.6720790	0.4652959	0.9707590	0.0341634
No Health Plan	0.7839949	0.6990701	0.8792366	0.0000318	1.0129620	0.8926615	1.149475	0.8417501	0.6631949	0.5954193	0.7386853	0.0000000	0.8277663	0.7560043	0.9063400	0.0000440	0.9154596	0.8392441	0.9985966	0.0464128	0.6248344	0.5712154	0.6834864	0.0000000
Unk. Health Plan	0.7566966	0.4957571	1.1549806	0.1963031	1.2310061	0.8519059	1.778807	0.2684785	0.4793732	0.3358660	0.6841974	0.0000511	0.9321440	0.6487282	1.3393782	0.7039811	0.6444596	0.4648099	0.8935443	0.0084127	1.0753962	0.7702044	1.5015196	0.6695098
No Personal Doctor	0.6455593	0.5795589	0.7190759	0.0000000	0.9273403	0.7943044	1.082658	0.3396970	0.4844152	0.4337420	0.5410085	0.0000000	0.5878698	0.5348488	0.6461469	0.0000000	0.8178738	0.7469890	0.8954851	0.0000138	0.7699015	0.6957930	0.8519034	0.0000004
Unk. Personal Doc	0.7596508	0.5514499	1.0464582	0.0925524	1.1600792	0.8105714	1.660290	0.4169104	0.5124193	0.3797313	0.6914721	0.0000123	0.6769646	0.4953412	0.9251827	0.0143703	0.7963063	0.5797205	1.0938094	0.1596264	0.6324627	0.4596955	0.8701610	0.0048886
No Checkup	0.6615904	0.6091174	0.7185837	0.0000000	0.9560849	0.8683007	1.052744	0.3607560	0.3691352	0.3394355	0.4014336	0.0000000	0.4633152	0.4333293	0.4953761	0.0000000	0.8948622	0.8347070	0.9593526	0.0017560	0.4776366	0.4466665	0.5107540	0.0000000
Unk. Checkup	0.5382582	0.4560887	0.6352316	0.0000000	1.1447833	0.9623475	1.361804	0.1268519	0.4340229	0.3696099	0.5096612	0.0000000	0.4697927	0.4122182	0.5354085	0.0000000	0.7053637	0.6221793	0.7996697	0.0000000	0.6260178	0.5492241	0.7135491	0.0000000
Cost Impacted Care	0.8617434	0.7968444	0.9319280	0.0001956	0.9243628	0.8409379	1.016064	0.1031566	0.8310644	0.7682554	0.8990084	0.0000039	0.7600332	0.7119165	0.8114021	0.0000000	1.0127611	0.9530306	1.0762351	0.6826556	0.7186076	0.6751759	0.7648330	0.0000000
Unk Cost	0.8155038	0.5849766	1.1368769	0.2289130	1.3445774	0.9327876	1.938157	0.1125097	0.5588881	0.4072983	0.7668971	0.0003134	0.5635641	0.3936358	0.8068485	0.0017355	0.8848785	0.6597807	1.1867729	0.4141457	1.0333878	0.7680486	1.3903943	0.8282699
Medicaid Expansion	0.9710817	0.9588004	0.9835203	0.0000062	0.9907906	0.9775016	1.004260	0.1793029	1.0482441	1.0329023	1.0638137	0.0000000	1.0211782	1.0100538	1.0324251	0.0001769	0.9963865	0.9873239	1.0055323	0.4374406	1.0049805	0.9946633	1.0154048	0.3453595

~R2

e1=1-bs$deviance/bs$null.deviance
e2=1-doc$deviance/doc$null.deviance
e3=1-hem$deviance/hem$null.deviance
e4=1-feet$deviance/feet$null.deviance 
e5=1-edu$deviance/edu$null.deviance
e6=1-eye$deviance/eye$null.deviance

effects=c(e1,e2,e3,e4,e5,e6)
effects=round(effects, 3)
names(effects)=c("Blood Sugar", "Doctor Visits", "HbA1c Checks",
                 'Feet Checks', "Diabetes Education", "Eye Checks")
myprint(effects,"Effect Sizes")

##        Blood Sugar      Doctor Visits       HbA1c Checks        Feet Checks 
##              0.020              0.001              0.125              0.042 
## Diabetes Education         Eye Checks 
##              0.033              0.054

Effect Sizes

	x
Blood Sugar	0.020
Doctor Visits	0.001
HbA1c Checks	0.125
Feet Checks	0.042
Diabetes Education	0.033
Eye Checks	0.054

psrsq(bs)

## [1] 0.01611201

psrsq(doc)

## [1] 0.000873653

psrsq(hem)

## [1] 0.1069256

psrsq(feet)

## [1] 0.04846467

psrsq(edu)

## [1] 0.04469911

psrsq(eye)

## [1] 0.06481921

Code Citations

mycite=function(x)citation(x)

A-D

mycite("car")

## 
## To cite the car package in publications use:
## 
##   John Fox and Sanford Weisberg (2019). An {R} Companion to Applied
##   Regression, Third Edition. Thousand Oaks CA: Sage. URL:
##   https://socialsciences.mcmaster.ca/jfox/Books/Companion/
## 
## A BibTeX entry for LaTeX users is
## 
##   @Book{,
##     title = {An {R} Companion to Applied Regression},
##     edition = {Third},
##     author = {John Fox and Sanford Weisberg},
##     year = {2019},
##     publisher = {Sage},
##     address = {Thousand Oaks {CA}},
##     url = {https://socialsciences.mcmaster.ca/jfox/Books/Companion/},
##   }

mycite("caret")

## 
## To cite package 'caret' in publications use:
## 
##   Max Kuhn (2020). caret: Classification and Regression Training. R
##   package version 6.0-86. https://CRAN.R-project.org/package=caret
## 
## A BibTeX entry for LaTeX users is
## 
##   @Manual{,
##     title = {caret: Classification and Regression Training},
##     author = {Max Kuhn},
##     year = {2020},
##     note = {R package version 6.0-86},
##     url = {https://CRAN.R-project.org/package=caret},
##   }

mycite("cdlTools") 

## 
## When citing cdlTools, please cite both the package and underlying data
## sources used:
## 
##   Chen, L., Lisic, J. (2018). Tools to Download and Work with USDA
##   Cropscape Data. R package version 0.14.
##   https://cran.r-project.org/package=cdlTools
## 
##   USDA-NASS. (2019) National Agricultural Statistics Service Cropland
##   Data Layer. https://nassgeodata.gmu.edu/CropScape/
## 
## To see these entries in BibTeX format, use 'print(<citation>,
## bibtex=TRUE)', 'toBibtex(.)', or set
## 'options(citation.bibtex.max=999)'.

mycite("corrplot")

## 
## To cite corrplot in publications use:
## 
##   Taiyun Wei and Viliam Simko (2017). R package "corrplot":
##   Visualization of a Correlation Matrix (Version 0.84). Available from
##   https://github.com/taiyun/corrplot
## 
## A BibTeX entry for LaTeX users is
## 
##   @Manual{corrplot2017,
##     title = {R package "corrplot": Visualization of a Correlation Matrix},
##     author = {Taiyun Wei and Viliam Simko},
##     year = {2017},
##     note = {(Version 0.84)},
##     url = {https://github.com/taiyun/corrplot},
##   }

mycite("data.table")

## 
## To cite package 'data.table' in publications use:
## 
##   Matt Dowle and Arun Srinivasan (2020). data.table: Extension of
##   `data.frame`. R package version 1.13.6.
##   https://CRAN.R-project.org/package=data.table
## 
## A BibTeX entry for LaTeX users is
## 
##   @Manual{,
##     title = {data.table: Extension of `data.frame`},
##     author = {Matt Dowle and Arun Srinivasan},
##     year = {2020},
##     note = {R package version 1.13.6},
##     url = {https://CRAN.R-project.org/package=data.table},
##   }

E-H

mycite("epitools")

## 
## To cite package 'epitools' in publications use:
## 
##   Tomas J. Aragon (2020). epitools: Epidemiology Tools. R package
##   version 0.5-10.1. https://CRAN.R-project.org/package=epitools
## 
## A BibTeX entry for LaTeX users is
## 
##   @Manual{,
##     title = {epitools: Epidemiology Tools},
##     author = {Tomas J. Aragon},
##     year = {2020},
##     note = {R package version 0.5-10.1},
##     url = {https://CRAN.R-project.org/package=epitools},
##   }

mycite("foreign")

## 
## To cite package 'foreign' in publications use:
## 
##   R Core Team (2020). foreign: Read Data Stored by 'Minitab', 'S',
##   'SAS', 'SPSS', 'Stata', 'Systat', 'Weka', 'dBase', .... R package
##   version 0.8-81. https://CRAN.R-project.org/package=foreign
## 
## A BibTeX entry for LaTeX users is
## 
##   @Manual{,
##     title = {foreign: Read Data Stored by 'Minitab', 'S', 'SAS', 'SPSS', 'Stata',
## 'Systat', 'Weka', 'dBase', ...},
##     author = {{R Core Team}},
##     year = {2020},
##     note = {R package version 0.8-81},
##     url = {https://CRAN.R-project.org/package=foreign},
##   }

mycite("fpp2")

## 
## To cite package 'fpp2' in publications use:
## 
##   Rob Hyndman (2020). fpp2: Data for "Forecasting: Principles and
##   Practice" (2nd Edition). R package version 2.4.
##   https://CRAN.R-project.org/package=fpp2
## 
## A BibTeX entry for LaTeX users is
## 
##   @Manual{,
##     title = {fpp2: Data for "Forecasting: Principles and Practice" (2nd Edition)},
##     author = {Rob Hyndman},
##     year = {2020},
##     note = {R package version 2.4},
##     url = {https://CRAN.R-project.org/package=fpp2},
##   }

mycite("gridExtra")

## 
## To cite package 'gridExtra' in publications use:
## 
##   Baptiste Auguie (2017). gridExtra: Miscellaneous Functions for "Grid"
##   Graphics. R package version 2.3.
##   https://CRAN.R-project.org/package=gridExtra
## 
## A BibTeX entry for LaTeX users is
## 
##   @Manual{,
##     title = {gridExtra: Miscellaneous Functions for "Grid" Graphics},
##     author = {Baptiste Auguie},
##     year = {2017},
##     note = {R package version 2.3},
##     url = {https://CRAN.R-project.org/package=gridExtra},
##   }

mycite("Hmisc")

## 
## To cite package 'Hmisc' in publications use:
## 
##   Frank E Harrell Jr, with contributions from Charles Dupont and many
##   others. (2020). Hmisc: Harrell Miscellaneous. R package version
##   4.4-2. https://CRAN.R-project.org/package=Hmisc
## 
## A BibTeX entry for LaTeX users is
## 
##   @Manual{,
##     title = {Hmisc: Harrell Miscellaneous},
##     author = {Frank E {Harrell Jr} and with contributions from Charles Dupont and many others.},
##     year = {2020},
##     note = {R package version 4.4-2},
##     url = {https://CRAN.R-project.org/package=Hmisc},
##   }
## 
## ATTENTION: This citation information has been auto-generated from the
## package DESCRIPTION file and may need manual editing, see
## 'help("citation")'.

I-L

mycite("jtools")

## 
## Long JA (2020). _jtools: Analysis and Presentation of Social Scientific
## Data_. R package version 2.1.0, <URL:
## https://cran.r-project.org/package=jtools>.
## 
## A BibTeX entry for LaTeX users is
## 
##   @Manual{jtools,
##     title = {jtools: Analysis and Presentation of Social Scientific Data},
##     author = {Jacob A. Long},
##     year = {2020},
##     note = {R package version 2.1.0},
##     url = {https://cran.r-project.org/package=jtools},
##   }

mycite("kableExtra")

## 
## To cite package 'kableExtra' in publications use:
## 
##   Hao Zhu (2020). kableExtra: Construct Complex Table with 'kable' and
##   Pipe Syntax. R package version 1.3.1.
##   https://CRAN.R-project.org/package=kableExtra
## 
## A BibTeX entry for LaTeX users is
## 
##   @Manual{,
##     title = {kableExtra: Construct Complex Table with 'kable' and Pipe Syntax},
##     author = {Hao Zhu},
##     year = {2020},
##     note = {R package version 1.3.1},
##     url = {https://CRAN.R-project.org/package=kableExtra},
##   }

mycite("lubridate")

## 
## To cite lubridate in publications use:
## 
##   Garrett Grolemund, Hadley Wickham (2011). Dates and Times Made Easy
##   with lubridate. Journal of Statistical Software, 40(3), 1-25. URL
##   https://www.jstatsoft.org/v40/i03/.
## 
## A BibTeX entry for LaTeX users is
## 
##   @Article{,
##     title = {Dates and Times Made Easy with {lubridate}},
##     author = {Garrett Grolemund and Hadley Wickham},
##     journal = {Journal of Statistical Software},
##     year = {2011},
##     volume = {40},
##     number = {3},
##     pages = {1--25},
##     url = {https://www.jstatsoft.org/v40/i03/},
##   }

M-Z

mycite("parameters")

## 
## Lüdecke D, Ben-Shachar M, Patil I, Makowski D (2020). "parameters:
## Extracting, Computing and Exploring the Parameters of Statistical
## Models using R." _Journal of Open Source Software_, *5*(53), 2445. doi:
## 10.21105/joss.02445 (URL: https://doi.org/10.21105/joss.02445).
## 
## A BibTeX entry for LaTeX users is
## 
##   @Article{,
##     title = {parameters: Extracting, Computing and Exploring the Parameters of Statistical Models using {R}.},
##     volume = {5},
##     doi = {10.21105/joss.02445},
##     number = {53},
##     journal = {Journal of Open Source Software},
##     author = {Daniel Lüdecke and Mattan S. Ben-Shachar and Indrajeet Patil and Dominique Makowski},
##     year = {2020},
##     pages = {2445},
##   }

mycite("plyr")

## 
## To cite plyr in publications use:
## 
##   Hadley Wickham (2011). The Split-Apply-Combine Strategy for Data
##   Analysis. Journal of Statistical Software, 40(1), 1-29. URL
##   http://www.jstatsoft.org/v40/i01/.
## 
## A BibTeX entry for LaTeX users is
## 
##   @Article{,
##     title = {The Split-Apply-Combine Strategy for Data Analysis},
##     author = {Hadley Wickham},
##     journal = {Journal of Statistical Software},
##     year = {2011},
##     volume = {40},
##     number = {1},
##     pages = {1--29},
##     url = {http://www.jstatsoft.org/v40/i01/},
##   }

mycite("psych")

## 
## To cite the psych package in publications use:
## 
##   Revelle, W. (2020) psych: Procedures for Personality and
##   Psychological Research, Northwestern University, Evanston, Illinois,
##   USA, https://CRAN.R-project.org/package=psych Version = 2.0.12,.
## 
## A BibTeX entry for LaTeX users is
## 
##   @Manual{,
##     title = {psych: Procedures for Psychological, Psychometric, and Personality Research},
##     author = {William Revelle},
##     organization = { Northwestern University},
##     address = { Evanston, Illinois},
##     year = {2020},
##     note = {R package version 2.0.12},
##     url = {https://CRAN.R-project.org/package=psych},
##   }

mycite("readr")

## 
## To cite package 'readr' in publications use:
## 
##   Hadley Wickham and Jim Hester (2020). readr: Read Rectangular Text
##   Data. R package version 1.4.0.
##   https://CRAN.R-project.org/package=readr
## 
## A BibTeX entry for LaTeX users is
## 
##   @Manual{,
##     title = {readr: Read Rectangular Text Data},
##     author = {Hadley Wickham and Jim Hester},
##     year = {2020},
##     note = {R package version 1.4.0},
##     url = {https://CRAN.R-project.org/package=readr},
##   }

mycite("remotes")

## 
## To cite package 'remotes' in publications use:
## 
##   Jim Hester, Gábor Csárdi, Hadley Wickham, Winston Chang, Martin
##   Morgan and Dan Tenenbaum (2020). remotes: R Package Installation from
##   Remote Repositories, Including 'GitHub'. R package version 2.2.0.
##   https://CRAN.R-project.org/package=remotes
## 
## A BibTeX entry for LaTeX users is
## 
##   @Manual{,
##     title = {remotes: R Package Installation from Remote Repositories, Including
## 'GitHub'},
##     author = {Jim Hester and Gábor Csárdi and Hadley Wickham and Winston Chang and Martin Morgan and Dan Tenenbaum},
##     year = {2020},
##     note = {R package version 2.2.0},
##     url = {https://CRAN.R-project.org/package=remotes},
##   }

mycite("survey")

## 
## To cite the survey package in publications use one or more of:
## 
##   T. Lumley (2020) "survey: analysis of complex survey samples". R
##   package version 4.0.
## 
##   T. Lumley (2004) Analysis of complex survey samples. Journal of
##   Statistical Software 9(1): 1-19
## 
##   T. Lumley (2010) Complex Surveys: A Guide to Analysis Using R. John
##   Wiley and Sons.
## 
## To see these entries in BibTeX format, use 'print(<citation>,
## bibtex=TRUE)', 'toBibtex(.)', or set
## 'options(citation.bibtex.max=999)'.

mycite("svrepmisc")

## Warning in citation(x): no date field in DESCRIPTION file of package 'svrepmisc'

## 
## To cite package 'svrepmisc' in publications use:
## 
##   Carl Ganz (2021). svrepmisc: svrepmisc: Miscellaneous Functions for
##   Replicate Weights. R package version 0.2.2.
##   https://github.com/carlganz/svrepmisc
## 
## A BibTeX entry for LaTeX users is
## 
##   @Manual{,
##     title = {svrepmisc: svrepmisc: Miscellaneous Functions for Replicate Weights},
##     author = {Carl Ganz},
##     year = {2021},
##     note = {R package version 0.2.2},
##     url = {https://github.com/carlganz/svrepmisc},
##   }

mycite("tidyverse")

## 
##   Wickham et al., (2019). Welcome to the tidyverse. Journal of Open
##   Source Software, 4(43), 1686, https://doi.org/10.21105/joss.01686
## 
## A BibTeX entry for LaTeX users is
## 
##   @Article{,
##     title = {Welcome to the {tidyverse}},
##     author = {Hadley Wickham and Mara Averick and Jennifer Bryan and Winston Chang and Lucy D'Agostino McGowan and Romain François and Garrett Grolemund and Alex Hayes and Lionel Henry and Jim Hester and Max Kuhn and Thomas Lin Pedersen and Evan Miller and Stephan Milton Bache and Kirill Müller and Jeroen Ooms and David Robinson and Dana Paige Seidel and Vitalie Spinu and Kohske Takahashi and Davis Vaughan and Claus Wilke and Kara Woo and Hiroaki Yutani},
##     year = {2019},
##     journal = {Journal of Open Source Software},
##     volume = {4},
##     number = {43},
##     pages = {1686},
##     doi = {10.21105/joss.01686},
##   }

mycite("viridis")

## 
## To cite package 'viridis' in publications use:
## 
##   Simon Garnier (2018). viridis: Default Color Maps from 'matplotlib'.
##   R package version 0.5.1. https://CRAN.R-project.org/package=viridis
## 
## A BibTeX entry for LaTeX users is
## 
##   @Manual{,
##     title = {viridis: Default Color Maps from 'matplotlib'},
##     author = {Simon Garnier},
##     year = {2018},
##     note = {R package version 0.5.1},
##     url = {https://CRAN.R-project.org/package=viridis},
##   }