Publication

  • Guerra-Centeno, D., Burmester-Ruiz, L., Guerra-Burmester, P., Guerra-Burmester, D., & Villatoro-Paz, F. (2020). Voluntary and mandatory use of face mask by pedestrians in Guatemala City during the COVID-19 pandemic. Ciencia, Tecnología y Salud, 7(3), 477–482. https://doi.org/10.36829/63cts.v7i3.894

Data

Summary table

ageclass Age mask.on none.improp Gender Decree pr_maskOn totals
a.age3 Adult 143 143 females a.Pre 0.50 286
a.age3 Adult 230 352 males a.Pre 0.40 582
a.age3 Adult 210 42 females b.Post 0.83 252
a.age3 Adult 460 125 males b.Post 0.79 585
age1 Kid 5 9 females a.Pre 0.36 14
age1 Kid 5 17 males a.Pre 0.23 22
age1 Kid 9 6 females b.Post 0.60 15
age1 Kid 20 8 males b.Post 0.71 28
age2 Juvenile 13 17 females a.Pre 0.43 30
age2 Juvenile 23 60 males a.Pre 0.28 83
age2 Juvenile 2 3 females b.Post 0.40 5
age2 Juvenile 46 6 males b.Post 0.88 52
age4 Elder 16 5 females a.Pre 0.76 21
age4 Elder 41 71 males a.Pre 0.37 112
age4 Elder 18 4 females b.Post 0.82 22
age4 Elder 32 17 males b.Post 0.65 49

Pooled tables

Exploratory plots

Se utiliza el Wilson Score (Agresti & Coull, 1998) para calcular los intervalos de confianza de las proporciones.

ageclass mask.on none.improp pr_maskOn totals lower upper
a.age3 1043 662 0.6117302 1705 0.5883707 0.6345874
age1 39 40 0.4936709 79 0.3863027 0.6016260
age2 84 86 0.4941176 170 0.4199266 0.5685687
age4 107 97 0.5245098 204 0.4561616 0.5919520

ageclass Gender mask.on none.improp pr_maskOn totals lower upper
a.age3 females 353 185 0.6561338 538 0.6150168 0.6950370
a.age3 males 690 477 0.5912596 1167 0.5628000 0.6191205
age1 females 14 15 0.4827586 29 0.3138609 0.6556898
age1 males 25 25 0.5000000 50 0.3664451 0.6335549
age2 females 15 20 0.4285714 35 0.2798457 0.5914259
age2 males 69 66 0.5111111 135 0.4276552 0.5939522
age4 females 34 9 0.7906977 43 0.6479436 0.8857717
age4 males 73 88 0.4534161 161 0.3784978 0.5305057

ageclass Decree mask.on none.improp pr_maskOn totals lower upper
a.age3 a.Pre 373 495 0.4297235 868 0.3971718 0.4628945
a.age3 b.Post 670 167 0.8004779 837 0.7720580 0.8261523
age1 a.Pre 10 26 0.2777778 36 0.1584834 0.4399249
age1 b.Post 29 14 0.6744186 43 0.5251621 0.7950670
age2 a.Pre 36 77 0.3185841 113 0.2398556 0.4092416
age2 b.Post 48 9 0.8421053 57 0.7263682 0.9146420
age4 a.Pre 57 76 0.4285714 133 0.3476376 0.5135156
age4 b.Post 50 21 0.7042254 71 0.5898146 0.7976711

Age+Decree+Gender Plot (Figura 2 de la publicación)

ageclass Age mask.on none.improp Gender Decree pr_maskOn totals lower upper
a.age3 Adult 143 143 females a.Pre 0.50 286 0.4424377 0.5575623
a.age3 Adult 230 352 males a.Pre 0.40 582 0.3562817 0.4354708
a.age3 Adult 210 42 females b.Post 0.83 252 0.7823885 0.8742681
a.age3 Adult 460 125 males b.Post 0.79 585 0.7512967 0.8176171
age1 Kid 5 9 females a.Pre 0.36 14 0.1634473 0.6123558
age1 Kid 5 17 males a.Pre 0.23 22 0.1012304 0.4343995
age1 Kid 9 6 females b.Post 0.60 15 0.3574683 0.8017550
age1 Kid 20 8 males b.Post 0.71 28 0.5294071 0.8474600
age2 Juvenile 13 17 females a.Pre 0.43 30 0.2737749 0.6080269
age2 Juvenile 23 60 males a.Pre 0.28 83 0.1923193 0.3816169
age2 Juvenile 2 3 females b.Post 0.40 5 0.1176208 0.7692757
age2 Juvenile 46 6 males b.Post 0.88 52 0.7702834 0.9460303
age4 Elder 16 5 females a.Pre 0.76 21 0.5490883 0.8937199
age4 Elder 41 71 males a.Pre 0.37 112 0.2826762 0.4583492
age4 Elder 18 4 females b.Post 0.82 22 0.6148339 0.9269312
age4 Elder 32 17 males b.Post 0.65 49 0.5131119 0.7707561
Figure 2.Proportion of individuals properly using face mask. The Wilson score (Agresti & Coull, 1998), is being used for showing binomial 95% confidence intervals.

Figure 2.Proportion of individuals properly using face mask. The Wilson score (Agresti & Coull, 1998), is being used for showing binomial 95% confidence intervals.

GLMs. Generalized logistic regression models (Agresti, 2007)

Additive models. Prediction for use of face mask by combinations of factors

Age + Gender + Decree

## 
## Call:
## glm(formula = mask ~ Decree + ageclass + Gender, family = binomial, 
##     data = mask2)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -1.9275  -1.0046   0.5825   0.7779   1.6719  
## 
## Coefficients:
##               Estimate Std. Error z value Pr(>|z|)    
## (Intercept)  -0.008761   0.096503  -0.091   0.9277    
## Decreepost    1.696804   0.099457  17.061   <2e-16 ***
## ageclassage1 -0.692747   0.252131  -2.748   0.0060 ** 
## ageclassage2 -0.235467   0.173846  -1.354   0.1756    
## ageclassage4 -0.108664   0.162065  -0.670   0.5025    
## Gendermales  -0.412245   0.105991  -3.889   0.0001 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 2921.5  on 2157  degrees of freedom
## Residual deviance: 2569.5  on 2152  degrees of freedom
## AIC: 2581.5
## 
## Number of Fisher Scoring iterations: 4

Age + Gender

## 
## Call:
## glm(formula = mask ~ ageclass + Gender, family = binomial, data = mask2)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -1.4712  -1.3335   0.9097   1.0290   1.2368  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept)   0.66837    0.08483   7.879  3.3e-15 ***
## ageclassage1 -0.49828    0.23119  -2.155  0.03114 *  
## ageclassage2 -0.44659    0.16184  -2.759  0.00579 ** 
## ageclassage4 -0.32598    0.14932  -2.183  0.02903 *  
## Gendermales  -0.30873    0.09796  -3.152  0.00162 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 2921.5  on 2157  degrees of freedom
## Residual deviance: 2895.2  on 2153  degrees of freedom
## AIC: 2905.2
## 
## Number of Fisher Scoring iterations: 4

Gender + Decree

## 
## Call:
## glm(formula = mask ~ Decree + Gender, family = binomial, data = mask2)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -1.9035  -0.9826   0.5973   0.7198   1.3856  
## 
## Coefficients:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept) -0.06226    0.09370  -0.664    0.506    
## Decreepost   1.69542    0.09851  17.210  < 2e-16 ***
## Gendermales -0.41487    0.10522  -3.943 8.05e-05 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 2921.5  on 2157  degrees of freedom
## Residual deviance: 2578.5  on 2155  degrees of freedom
## AIC: 2584.5
## 
## Number of Fisher Scoring iterations: 4

Age + Decree

## 
## Call:
## glm(formula = mask ~ Decree + ageclass, family = binomial, data = mask2)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -1.7960  -1.0595   0.6668   0.7534   1.5981  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept)  -0.28371    0.06586  -4.308 1.65e-05 ***
## Decreepost    1.67417    0.09872  16.959  < 2e-16 ***
## ageclassage1 -0.66637    0.25120  -2.653  0.00798 ** 
## ageclassage2 -0.27619    0.17375  -1.590  0.11194    
## ageclassage4 -0.15843    0.16023  -0.989  0.32280    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 2921.5  on 2157  degrees of freedom
## Residual deviance: 2584.8  on 2153  degrees of freedom
## AIC: 2594.8
## 
## Number of Fisher Scoring iterations: 4

Delta AICs. How much the additive model “losses” with each variable removal

AICs

Global model (GM)
## [1] 2581.522
GM without Decree
## [1] 2905.212
GM without Age
## [1] 2584.543
GM without Gender
## [1] 2594.861

Delta AICs Table

Model Delta.AIC
Global 0.00
Without.Decree -323.69
Without.Age -3.02
Without.Gender -13.34

ORs table for global additive model (intercepts not included)

##      Variable Lower Odds Ratio Upper
## 1 Post-Decree   4.5        5.5   6.6
## 2    Children   0.3        0.5   0.8
## 3   Juveniles   0.6        0.8   1.1
## 4      Elders   0.7        0.9   1.2
## 5       Males   0.5        0.7   0.8

Interaction model

## 
## Call:
## glm(formula = mask ~ Decree * ageclass * Gender, family = binomial, 
##     data = mask2)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -2.0782  -1.0028   0.6039   0.6934   1.7214  
## 
## Coefficients:
##                                       Estimate Std. Error z value Pr(>|z|)    
## (Intercept)                          2.112e-14  1.183e-01   0.000  1.00000    
## Decreepost                           1.609e+00  2.063e-01   7.802 6.11e-15 ***
## ageclassage1                        -5.878e-01  5.702e-01  -1.031  0.30259    
## ageclassage2                        -2.683e-01  3.870e-01  -0.693  0.48814    
## ageclassage4                         1.163e+00  5.258e-01   2.212  0.02696 *  
## Gendermales                         -4.256e-01  1.455e-01  -2.924  0.00345 ** 
## Decreepost:ageclassage1             -6.162e-01  7.946e-01  -0.775  0.43808    
## Decreepost:ageclassage2             -1.747e+00  1.006e+00  -1.737  0.08246 .  
## Decreepost:ageclassage4             -1.269e+00  7.814e-01  -1.623  0.10452    
## Decreepost:Gendermales               1.190e-01  2.448e-01   0.486  0.62679    
## ageclassage1:Gendermales            -2.104e-01  7.688e-01  -0.274  0.78431    
## ageclassage2:Gendermales            -2.650e-01  4.659e-01  -0.569  0.56945    
## ageclassage4:Gendermales            -1.287e+00  5.676e-01  -2.267  0.02339 *  
## Decreepost:ageclassage1:Gendermales  1.028e+00  1.040e+00   0.988  0.32326    
## Decreepost:ageclassage2:Gendermales  3.014e+00  1.130e+00   2.667  0.00766 ** 
## Decreepost:ageclassage4:Gendermales  7.217e-01  8.698e-01   0.830  0.40670    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 2921.5  on 2157  degrees of freedom
## Residual deviance: 2544.4  on 2142  degrees of freedom
## AIC: 2576.4
## 
## Number of Fisher Scoring iterations: 4

Generalized Logistic MIXED Model

Decree as Random effect

## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: Y ~ Gender * ageclass + (1 | Decree)
##    Data: mask
## 
##      AIC      BIC   logLik deviance df.resid 
##    115.7    122.7    -48.9     97.7        7 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -1.8882 -0.7442 -0.0418  0.6617  2.2326 
## 
## Random effects:
##  Groups Name        Variance Std.Dev.
##  Decree (Intercept) 0.7065   0.8406  
## Number of obs: 16, groups:  Decree, 2
## 
## Fixed effects:
##                          Estimate Std. Error z value Pr(>|z|)   
## (Intercept)                0.8169     0.6026   1.356  0.17521   
## Gendermales               -0.3822     0.1172  -3.260  0.00111 **
## ageclassage1              -0.9308     0.4167  -2.234  0.02548 * 
## ageclassage2              -0.4979     0.3690  -1.350  0.17716   
## ageclassage4               0.6969     0.4059   1.717  0.08601 . 
## Gendermales:ageclassage1   0.3839     0.5214   0.736  0.46156   
## Gendermales:ageclassage2   0.3294     0.4179   0.788  0.43061   
## Gendermales:ageclassage4  -0.9866     0.4448  -2.218  0.02656 * 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) Gndrml agcls1 agcls2 agcls4 Gndr:1 Gndr:2
## Gendermales -0.137                                          
## ageclassag1 -0.039  0.198                                   
## ageclassag2 -0.041  0.215  0.059                            
## ageclassag4 -0.038  0.198  0.055  0.064                     
## Gndrmls:gc1  0.030 -0.224 -0.797 -0.049 -0.045              
## Gndrmls:gc2  0.037 -0.276 -0.053 -0.882 -0.056  0.062       
## Gndrmls:gc4  0.036 -0.263 -0.052 -0.057 -0.912  0.059  0.073

Gender as random effect

## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: Y ~ Decree * ageclass + (1 | Gender)
##    Data: mask
## 
##      AIC      BIC   logLik deviance df.resid 
##    109.3    116.3    -45.7     91.3        7 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.3695 -0.3845  0.0019  0.6123  2.3290 
## 
## Random effects:
##  Groups Name        Variance Std.Dev.
##  Gender (Intercept) 0.04101  0.2025  
## Number of obs: 16, groups:  Gender, 2
## 
## Fixed effects:
##                           Estimate Std. Error z value Pr(>|z|)    
## (Intercept)               -0.22021    0.15994  -1.377   0.1686    
## Decreeb.Post               1.69554    0.11116  15.254   <2e-16 ***
## ageclassage1              -0.70151    0.38006  -1.846   0.0649 .  
## ageclassage2              -0.45632    0.21411  -2.131   0.0331 *  
## ageclassage4               0.06160    0.18978   0.325   0.7455    
## Decreeb.Post:ageclassage1  0.01867    0.50852   0.037   0.9707    
## Decreeb.Post:ageclassage2  0.81689    0.43095   1.896   0.0580 .  
## Decreeb.Post:ageclassage4 -0.58968    0.33423  -1.764   0.0777 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) Dcrb.P agcls1 agcls2 agcls4 Dc.P:1 Dc.P:2
## Decreeb.Pst -0.257                                          
## ageclassag1 -0.081  0.110                                   
## ageclassag2 -0.136  0.201  0.058                            
## ageclassag4 -0.144  0.234  0.063  0.119                     
## Dcrb.Pst:g1  0.059 -0.216 -0.747 -0.043 -0.049              
## Dcrb.Pst:g2  0.073 -0.253 -0.030 -0.496 -0.054  0.056       
## Dcrb.Pst:g4  0.081 -0.336 -0.036 -0.068 -0.569  0.072  0.083

References

  1. Agresti, A. (2007). An Introduction to Categorical Data Analysis. JohnWiley and Sons.

  2. Agresti, A., & Coull, B. A. (1998). Approximate is better than “exact” for interval estimation of binomial proportions. The American Statistician, 52(2), 119–126.