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Table 2 Distribution of glycemic control indicator among predictors, pooled over years and clinics

From: Application of machine learning methodology to assess the performance of DIABETIMSS program for patients with type 2 diabetes in family medicine clinics in Mexico

Variables

HbA1c > =7%

HbA1c < 7%

Missing

Adjusted

p-value

Referred to DIABETIMSS, n (prop.)

   

< 0.001

 No

63284 (0.50)

31225 (0.24)

33258 (0.26)

 

 Yes

16254 (0.54)

8940 (0.30)

4797 (0.16)

 

 Missing

65391 (0.21)

23556 (0.08)

224410 (0.72)

 

Previous glycemic control, n (prop.)

   

<0.001

 No

69031 (0.60)

15161 (0.13)

30918 (0.27)

 

 Yes

12707 (0.26)

19724 (0.41)

15628 (0.33)

 

 Missing

63191 (0.21)

28836 (0.09)

215919 (0.70)

 

Age, n (prop.)

   

< 0.001

 [0,53)

39172 (0.55)

12795 (0.18)

19186 (0.27)

 

 [53,62)

37784 (0.53)

14612 (0.21)

18642 (0.26)

 

 [62,71)

38587 (0.53)

18023 (0.25)

16378 (0.22)

 

 [71, 116]

29386 (0.47)

18291 (0.29)

15395 (0.24)

 

 Missing

0 (0.00)

0 (0.00)

192864 (1.00)

 

Nutrition status at the beginning of the year, n (prop.)

   

0.646

 Underweight

462 (0.44)

190 (0.18)

409 (0.39)

 

 Normal weight

24399 (0.51)

10454 (0.22)

13326 (0.28)

 

 Overweight

59249 (0.52)

25584 (0.23)

28164 (0.25)

 

 Obesity

60609 (0.53)

27360 (0.24)

27228 (0.24)

 

 Missing

210 (0.00)

133 (0.00)

193338 (1.00)

 

Sex, n (prop.)

   

0.004

 Female

86565 (0.52)

38609 (0.23)

40071 (0.24)

 

 Male

58364 (0.52)

25112 (0.22)

29530 (0.26)

 

 Missing

0 (0.00)

0 (0.00)

192864 (1.00)

 

BMI at the beginning of the year (kg/m2), n (prop.)

   

0.901

 [11.2, 26.0)

36448 (0.51)

15636 (0.22)

19581 (0.27)

 

 [26.0, 28.9)

36040 (0.53)

15495 (0.23)

16969 (0.25)

 

 [28.9, 32.4)

36242 (0.53)

15979 (0.23)

16096 (0.24)

 

 [32.4, 85.4]

35989 (0.52)

16478 (0.24)

16481 (0.24)

 

 Missing

210 (0.00)

133 (0.00)

193338 (1.00)

 

Height at the beginning of the year (m), n (prop.)

   

0.003

 [1.30, 1.50)

37877 (0.54)

16438 (0.23)

16342 (0.23)

 

 [1.50, 1.57)

39267 (0.53)

17393 (0.23)

17526 (0.24)

 

 [1.57, 1.64)

33231 (0.52)

14773 (0.23)

16363 (0.25)

 

 [1.64, 2.10]

34344 (0.50)

14984 (0.22)

18896 (0.28)

 

 Missing

210 (0.00)

133 (0.00)

193338 (1.00)

 

Weight at the beginning of the year (kg), n (prop.)

   

< 0.001

 [30, 63)

37150 (0.52)

15744 (0.22)

18099 (0.25)

[30, 63)

 [63, 72)

36771 (0.52)

16188 (0.23)

17106 (0.24)

[63, 72)

 [72, 82)

35912 (0.53)

15694 (0.23)

16594 (0.24)

[72, 82)

 [82, 198]

34886 (0.51)

15962 (0.23)

17328 (0.25)

[82, 198]

 Missing

210 (0.00)

133 (0.00)

193338 (1.00)

 

Obesity, n (prop.)

   

0.247

 No

24861 (0.50)

10644 (0.22)

13735 (0.28)

 

 Yes

119858 (0.53)

52944 (0.23)

55392 (0.24)

 

 Missing

210 (0.00)

133 (0.00)

193338 (1.00)

 

Patients with Risk Factors (smoking, hypertension, dyslipidemia), n (prop.)

   

0.022

 No

24358 (0.50)

9576 (0.20)

14853 (0.30)

 

 Yes

120571 (0.53)

54145 (0.24)

54748 (0.24)

 

 Missing

0 (0.00)

0 (0.00)

192864 (1.00)

 

Smoking Habit, n (prop.)

   

< 0.001

 No

141903 (0.52)

62119 (0.23)

68196 (0.25)

 

 Yes

3026 (0.50)

1602 (0.27)

1405 (0.23)

 

 Missing

0 (0.00)

0 (0.00)

192864 (1.00)

 

Type of insurance, n (prop.)

   

0.027

 Others

74686 (0.53)

31175 (0.22)

36123 (0.25)

 

 Parents insured/Retired

70243 (0.52)

32546 (0.24)

33478 (0.25)

 

 Missing

0 (0.00)

0 (0.00)

192864 (1.00)

 

Year, n (prop.)

   

<0.001

 2012

28445 (0.30)

11297 (0.12)

54481 (0.58)

 

 2013

27127 (0.29)

10916 (0.12)

56180 (0.60)

 

 2014

29070 (0.31)

12264 (0.13)

52889 (0.56)

 

 2015

30468 (0.32)

13582 (0.14)

50173 (0.53)

 

 2016

29819 (0.32)

15662 (0.17)

48742 (0.52)

 

Total number of diabetes complications, n (prop.)

   

<0.001

 0

77522 (0.50)

36812 (0.24)

40760 (0.26)

 

 1

45655 (0.54)

19015 (0.22)

20236 (0.24)

 

 >1

21752 (0.57)

7894 (0.21)

8605 22)

 
  1. The adjusted p-value is derived by tting a generalized estimating equations (GEE) with all the predictors, adjusting for patient ID. Then we did analysis of ‘Wald statistic’ with binomial model and logit link to obtain the p-value. Specifically, the R function is: t = geeglm (formula = indic10 curr diabetimss + edad + sexo + tipo pac + anttab + pesoini + tallaini + imcIni + EdoNutricioIni + facriesg + tot enfcrondiab + SobObes + indic10 prev + year, family = binomial (link = “logit”), data = all complete, id = a l, corstr = “exchangable”, std.err = “san.se” anova (t)