Projeto covid1 regressão

Instalação de pacotes e extração de dados

Packages

Library

Criando tabela casos

Filtrando somente casos de covid

casos_fil_covid <- filter(casos_fil, CLASSI_FIN == 5)
rm(casos_fil)
gc()

##             used   (Mb) gc trigger   (Mb)  max used   (Mb)
## Ncells   1448919   77.4    2705755  144.6   2394751  127.9
## Vcells 167960362 1281.5  632284791 4824.0 660739455 5041.1

Criando a coluna menor data

Summary do df

summary(casos_fil_covid)

##    DT_NOTIFIC            SEM_NOT       SG_UF_NOT          ID_MUNICIP       
##  Min.   :2020-02-21   Min.   : 1.00   Length:2136005     Length:2136005    
##  1st Qu.:2020-11-11   1st Qu.:12.00   Class :character   Class :character  
##  Median :2021-03-24   Median :21.00   Mode  :character   Mode  :character  
##  Mean   :2021-03-17   Mean   :22.53                                        
##  3rd Qu.:2021-06-15   3rd Qu.:30.00                                        
##  Max.   :2022-12-09   Max.   :53.00                                        
##                                                                            
##    CO_MUN_NOT       CS_SEXO            DT_NASC            CS_GESTANT   
##  Min.   :110001   Length:2136005     Length:2136005     Min.   :0.000  
##  1st Qu.:310620   Class :character   Class :character   1st Qu.:5.000  
##  Median :351880   Mode  :character   Mode  :character   Median :6.000  
##  Mean   :344069                                         Mean   :5.779  
##  3rd Qu.:410690                                         3rd Qu.:6.000  
##  Max.   :530010                                         Max.   :9.000  
##                                                                        
##     CS_RACA        CS_ESCOL_N      PAC_COCBO            CS_ZONA      
##  Min.   :1.00    Min.   :0.0      Length:2136005     Min.   :1.00    
##  1st Qu.:1.00    1st Qu.:2.0      Class :character   1st Qu.:1.00    
##  Median :4.00    Median :4.0      Mode  :character   Median :1.00    
##  Mean   :3.49    Mean   :5.4                         Mean   :1.15    
##  3rd Qu.:4.00    3rd Qu.:9.0                         3rd Qu.:1.00    
##  Max.   :9.00    Max.   :9.0                         Max.   :9.00    
##  NA's   :28576   NA's   :716885                      NA's   :233075  
##    VACINA_COV         VACINA        DT_UT_DOSE           HOSPITAL    
##  Min.   :1.0      Min.   :1.0      Length:2136005     Min.   :1.00   
##  1st Qu.:2.0      1st Qu.:2.0      Class :character   1st Qu.:1.00   
##  Median :2.0      Median :2.0      Mode  :character   Median :1.00   
##  Mean   :2.6      Mean   :5.1                         Mean   :1.04   
##  3rd Qu.:2.0      3rd Qu.:9.0                         3rd Qu.:1.00   
##  Max.   :9.0      Max.   :9.0                         Max.   :9.00   
##  NA's   :326476   NA's   :528302                      NA's   :45286  
##    DT_INTERNA              UTI           DT_ENTUTI         
##  Min.   :2020-01-05   Min.   :1.00     Min.   :2020-01-05  
##  1st Qu.:2020-11-06   1st Qu.:1.00     1st Qu.:2020-11-09  
##  Median :2021-03-21   Median :2.00     Median :2021-03-24  
##  Mean   :2021-03-23   Mean   :1.79     Mean   :2021-03-17  
##  3rd Qu.:2021-06-11   3rd Qu.:2.00     3rd Qu.:2021-06-14  
##  Max.   :9202-09-11   Max.   :9.00     Max.   :4202-05-26  
##                       NA's   :254657                       
##    DT_SAIDUTI           CLASSI_FIN    EVOLUCAO       DT_EVOLUCA        
##  Min.   :2020-02-21   Min.   :5    Min.   :1.0     Min.   :2020-02-21  
##  1st Qu.:2020-11-12   1st Qu.:5    1st Qu.:1.0     1st Qu.:2020-11-17  
##  Median :2021-03-26   Median :5    Median :1.0     Median :2021-03-31  
##  Mean   :2021-03-18   Mean   :5    Mean   :1.5     Mean   :2021-03-23  
##  3rd Qu.:2021-06-16   3rd Qu.:5    3rd Qu.:2.0     3rd Qu.:2021-06-21  
##  Max.   :2121-03-13   Max.   :5    Max.   :9.0     Max.   :2022-12-04  
##                                    NA's   :99481                       
##    NU_IDADE_N       NOSOCOMIAL       AVE_SUINO          FEBRE       
##  Min.   : -9.00   Min.   :1.0      Min.   :1.0      Min.   :1.0     
##  1st Qu.: 45.00   1st Qu.:2.0      1st Qu.:2.0      1st Qu.:1.0     
##  Median : 59.00   Median :2.0      Median :2.0      Median :1.0     
##  Mean   : 57.94   Mean   :2.6      Mean   :3.3      Mean   :1.4     
##  3rd Qu.: 72.00   3rd Qu.:2.0      3rd Qu.:2.0      3rd Qu.:2.0     
##  Max.   :150.00   Max.   :9.0      Max.   :9.0      Max.   :9.0     
##                   NA's   :375647   NA's   :377867   NA's   :341238  
##      TOSSE           GARGANTA         DISPNEIA        DESC_RESP     
##  Min.   :1.0      Min.   :1        Min.   :1.00     Min.   :1.0     
##  1st Qu.:1.0      1st Qu.:2        1st Qu.:1.00     1st Qu.:1.0     
##  Median :1.0      Median :2        Median :1.00     Median :1.0     
##  Mean   :1.3      Mean   :2        Mean   :1.28     Mean   :1.4     
##  3rd Qu.:1.0      3rd Qu.:2        3rd Qu.:1.00     3rd Qu.:2.0     
##  Max.   :9.0      Max.   :9        Max.   :9.00     Max.   :9.0     
##  NA's   :269458   NA's   :617102   NA's   :266184   NA's   :410120  
##    SATURACAO         DIARREIA          VOMITO          DOR_ABD      
##  Min.   :1.0      Min.   :1        Min.   :1.0      Min.   :1.0     
##  1st Qu.:1.0      1st Qu.:2        1st Qu.:2.0      1st Qu.:2.0     
##  Median :1.0      Median :2        Median :2.0      Median :2.0     
##  Mean   :1.4      Mean   :2        Mean   :2.1      Mean   :2.2     
##  3rd Qu.:2.0      3rd Qu.:2        3rd Qu.:2.0      3rd Qu.:2.0     
##  Max.   :9.0      Max.   :9        Max.   :9.0      Max.   :9.0     
##  NA's   :342699   NA's   :645448   NA's   :673453   NA's   :875489  
##      FADIGA         PERD_OLFT        PERD_PALA        OUTRO_SIN     
##  Min.   :1.0      Min.   :1.0      Min.   :1.0      Min.   :1.0     
##  1st Qu.:1.0      1st Qu.:2.0      1st Qu.:2.0      1st Qu.:1.0     
##  Median :2.0      Median :2.0      Median :2.0      Median :2.0     
##  Mean   :1.9      Mean   :2.1      Mean   :2.1      Mean   :1.8     
##  3rd Qu.:2.0      3rd Qu.:2.0      3rd Qu.:2.0      3rd Qu.:2.0     
##  Max.   :9.0      Max.   :9.0      Max.   :9.0      Max.   :9.0     
##  NA's   :797981   NA's   :854395   NA's   :855749   NA's   :618960  
##   OUTRO_DES          FATOR_RISC           PUERPERA         CARDIOPATI     
##  Length:2136005     Length:2136005     Min.   :1.0       Min.   :1.0      
##  Class :character   Class :character   1st Qu.:2.0       1st Qu.:1.0      
##  Mode  :character   Mode  :character   Median :2.0       Median :1.0      
##                                        Mean   :2.2       Mean   :1.4      
##                                        3rd Qu.:2.0       3rd Qu.:2.0      
##                                        Max.   :9.0       Max.   :9.0      
##                                        NA's   :1355981   NA's   :1101849  
##    HEMATOLOGI        SIND_DOWN          HEPATICA            ASMA        
##  Min.   :1.0       Min.   :1.0       Min.   :1.0       Min.   :1.0      
##  1st Qu.:2.0       1st Qu.:2.0       1st Qu.:2.0       1st Qu.:2.0      
##  Median :2.0       Median :2.0       Median :2.0       Median :2.0      
##  Mean   :2.2       Mean   :2.2       Mean   :2.2       Mean   :2.1      
##  3rd Qu.:2.0       3rd Qu.:2.0       3rd Qu.:2.0       3rd Qu.:2.0      
##  Max.   :9.0       Max.   :9.0       Max.   :9.0       Max.   :9.0      
##  NA's   :1353650   NA's   :1356345   NA's   :1355968   NA's   :1343897  
##     DIABETES         NEUROLOGIC        PNEUMOPATI        IMUNODEPRE     
##  Min.   :1.0       Min.   :1.0       Min.   :1.0       Min.   :1.0      
##  1st Qu.:1.0       1st Qu.:2.0       1st Qu.:2.0       1st Qu.:2.0      
##  Median :2.0       Median :2.0       Median :2.0       Median :2.0      
##  Mean   :1.6       Mean   :2.1       Mean   :2.1       Mean   :2.1      
##  3rd Qu.:2.0       3rd Qu.:2.0       3rd Qu.:2.0       3rd Qu.:2.0      
##  Max.   :9.0       Max.   :9.0       Max.   :9.0       Max.   :9.0      
##  NA's   :1175363   NA's   :1333968   NA's   :1335383   NA's   :1345752  
##      RENAL           OBESIDADE         OBES_IMC           OUT_MORBI      
##  Min.   :1.0       Min.   :1         Length:2136005     Min.   :1.0      
##  1st Qu.:2.0       1st Qu.:2         Class :character   1st Qu.:1.0      
##  Median :2.0       Median :2         Mode  :character   Median :1.0      
##  Mean   :2.1       Mean   :2                            Mean   :1.5      
##  3rd Qu.:2.0       3rd Qu.:2                            3rd Qu.:2.0      
##  Max.   :9.0       Max.   :9                            Max.   :9.0      
##  NA's   :1337435   NA's   :1306587                      NA's   :1176060  
##   MORB_DESC          DOSE_1_COV         DOSE_2_COV          DOSE_REF        
##  Length:2136005     Length:2136005     Length:2136005     Length:2136005    
##  Class :character   Class :character   Class :character   Class :character  
##  Mode  :character   Mode  :character   Mode  :character   Mode  :character  
##                                                                             
##                                                                             
##                                                                             
##                                                                             
##   FAB_COVREF          ANTIVIRAL        TP_ANTIVIR       OUT_ANTIV        
##  Length:2136005     Min.   :1.0      Min.   :1.0       Length:2136005    
##  Class :character   1st Qu.:2.0      1st Qu.:1.0       Class :character  
##  Mode  :character   Median :2.0      Median :1.0       Mode  :character  
##                     Mean   :3.2      Mean   :1.2                         
##                     3rd Qu.:2.0      3rd Qu.:1.0                         
##                     Max.   :9.0      Max.   :3.0                         
##                     NA's   :348281   NA's   :2012569                     
##   DT_ANTIVIR          SUPORT_VEN       RAIOX_RES       RAIOX_OUT        
##  Length:2136005     Min.   :1.00     Min.   :1.0      Length:2136005    
##  Class :character   1st Qu.:2.00     1st Qu.:3.0      Class :character  
##  Mode  :character   Median :2.00     Median :6.0      Mode  :character  
##                     Mean   :2.26     Mean   :5.3                        
##                     3rd Qu.:2.00     3rd Qu.:6.0                        
##                     Max.   :9.00     Max.   :9.0                        
##                     NA's   :257209   NA's   :859382                     
##     TOMO_RES        TOMO_OUT            AMOSTRA        TP_AMOSTRA    
##  Min.   :1.0      Length:2136005     Min.   :1.00    Min.   :1.00    
##  1st Qu.:1.0      Class :character   1st Qu.:1.00    1st Qu.:1.00    
##  Median :1.0      Mode  :character   Median :1.00    Median :1.00    
##  Mean   :3.1                         Mean   :1.08    Mean   :1.38    
##  3rd Qu.:6.0                         3rd Qu.:1.00    3rd Qu.:1.00    
##  Max.   :9.0                         Max.   :9.00    Max.   :9.00    
##  NA's   :864905                      NA's   :71983   NA's   :185218  
##   OUT_AMOST           PCR_RESUL         DT_PCR            POS_PCRFLU     
##  Length:2136005     Min.   :1.00     Length:2136005     Min.   :1.0      
##  Class :character   1st Qu.:1.00     Class :character   1st Qu.:2.0      
##  Mode  :character   Median :1.00     Mode  :character   Median :2.0      
##                     Mean   :1.89                        Mean   :2.8      
##                     3rd Qu.:2.00                        3rd Qu.:2.0      
##                     Max.   :9.00                        Max.   :9.0      
##                     NA's   :195685                      NA's   :1320192  
##    TP_FLU_PCR        PCR_FLUASU         DT_MIN.DT_MIN    
##  Min.   :1.0       Min.   :1.0       Min.   :2020-01-05  
##  1st Qu.:1.0       1st Qu.:2.0       1st Qu.:2020-11-05  
##  Median :1.0       Median :2.0       Median :2021-03-21  
##  Mean   :1.1       Mean   :2.5       Mean   :2021-03-12  
##  3rd Qu.:1.0       3rd Qu.:3.0       3rd Qu.:2021-06-10  
##  Max.   :2.0       Max.   :6.0       Max.   :2022-12-04  
##  NA's   :2135015   NA's   :2135160

Deixando nascimento em formato de data para o R e analisando a coluna NU_IDADE_N. Podemos ver que algumas pessoas possuem idades não condizentes com a realidade, com idades menores do que 0 ou muito acima da pessoa mais velha registrada no Brasil, portanto limito a 100 anos de idade.

casos_fil_covid$DT_NASC<- as_date(casos_fil_covid$DT_NASC, format= "%d/%m/%Y")

count(casos_fil_covid, NU_IDADE_N <= 0)

## # A tibble: 2 x 2
##   `NU_IDADE_N <= 0`       n
##   <lgl>               <int>
## 1 FALSE             2134830
## 2 TRUE                 1175

count(casos_fil_covid, NU_IDADE_N >= 100)

## # A tibble: 2 x 2
##   `NU_IDADE_N >= 100`       n
##   <lgl>                 <int>
## 1 FALSE               2132815
## 2 TRUE                   3190

casos_fil_covid <- filter(casos_fil_covid, NU_IDADE_N > 0)
casos_fil_covid <- filter(casos_fil_covid, NU_IDADE_N < 100)

Criando tabelas por tipo (Possuimos muitas colunas, algumas delas com muitos nulos, portanto para verificar quais possuem impacto na mortalidade vou separar por tipo, por exemplo, demográficas, comorbidades, relacionadas a vacinação, etc.)

Demográficas e economicas

casos_fil_covid_dem <- casos_fil_covid %>% select(DT_MIN, SG_UF_NOT, ID_MUNICIP, CO_MUN_NOT, CS_SEXO, CS_GESTANT, CS_RACA, CS_ESCOL_N, PAC_COCBO, CS_ZONA, NU_IDADE_N, EVOLUCAO)

Summary do df demográfico

summary(casos_fil_covid_dem)

##     DT_MIN.DT_MIN      SG_UF_NOT          ID_MUNICIP          CO_MUN_NOT    
##  Min.   :2020-01-05   Length:2131640     Length:2131640     Min.   :110001  
##  1st Qu.:2020-11-05   Class :character   Class :character   1st Qu.:310620  
##  Median :2021-03-21   Mode  :character   Mode  :character   Median :351880  
##  Mean   :2021-03-12                                         Mean   :344123  
##  3rd Qu.:2021-06-10                                         3rd Qu.:410690  
##  Max.   :2022-12-04                                         Max.   :530010  
##                                                                             
##    CS_SEXO            CS_GESTANT       CS_RACA        CS_ESCOL_N    
##  Length:2131640     Min.   :0.000   Min.   :1.00    Min.   :0.0     
##  Class :character   1st Qu.:5.000   1st Qu.:1.00    1st Qu.:2.0     
##  Mode  :character   Median :6.000   Median :4.00    Median :4.0     
##                     Mean   :5.779   Mean   :3.49    Mean   :5.4     
##                     3rd Qu.:6.000   3rd Qu.:4.00    3rd Qu.:9.0     
##                     Max.   :9.000   Max.   :9.00    Max.   :9.0     
##                                     NA's   :28517   NA's   :715644  
##   PAC_COCBO            CS_ZONA         NU_IDADE_N       EVOLUCAO    
##  Length:2131640     Min.   :1.00     Min.   : 1.00   Min.   :1.0    
##  Class :character   1st Qu.:1.00     1st Qu.:45.00   1st Qu.:1.0    
##  Mode  :character   Median :1.00     Median :59.00   Median :1.0    
##                     Mean   :1.15     Mean   :57.91   Mean   :1.5    
##                     3rd Qu.:1.00     3rd Qu.:72.00   3rd Qu.:2.0    
##                     Max.   :9.00     Max.   :99.00   Max.   :9.0    
##                     NA's   :232617                   NA's   :99271

Estruturando em one hot encoded para poder fazer a regressão logit.

casos_fil_covid_dem <- dummy_cols(casos_fil_covid_dem, select_columns = c('CS_SEXO', 'CS_GESTANT', 'CS_RACA', 'CS_ZONA'),
           remove_selected_columns = TRUE)

Transformando a variável EVOLUCAO em dummy, 1 = sobreviveu, 0 = óbito.

casos_fil_covid_dem$EVOLUCAO <- if_else(casos_fil_covid_dem$EVOLUCAO == 1 , 1, 0)

Porcentagem de nulos na coluna EVOLUCAO.

sum(is.na(casos_fil_covid$EVOLUCAO)) / length(casos_fil_covid$EVOLUCAO)

## [1] 0.04657025

Variáveis selecionadas: CS_SEX_F = se é mulher ou não, CS_RACA_2 = se é preto ou não, CS_ZONA_2 = se é rural ou não, NU_IDADE_N = idade da pessoa. EVOLUCAO = 1 para sobreviveu, 0 para óbito. Recordo também que o comando glm omite as linhas com valores nulos.

casos_fil_covid.logit <- glm(formula = EVOLUCAO ~   CS_SEXO_F + CS_RACA_2  + CS_ZONA_2 + NU_IDADE_N, family = binomial(link = "logit"), 
    data = casos_fil_covid_dem)
summary(casos_fil_covid.logit)

## 
## Call:
## glm(formula = EVOLUCAO ~ CS_SEXO_F + CS_RACA_2 + CS_ZONA_2 + 
##     NU_IDADE_N, family = binomial(link = "logit"), data = casos_fil_covid_dem)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -2.4794  -1.1620   0.6629   0.9259   1.7909  
## 
## Coefficients:
##               Estimate Std. Error z value Pr(>|z|)    
## (Intercept)  2.921e+00  6.452e-03  452.77   <2e-16 ***
## CS_SEXO_F    1.441e-01  3.330e-03   43.26   <2e-16 ***
## CS_RACA_2   -3.064e-01  7.815e-03  -39.20   <2e-16 ***
## CS_ZONA_2   -1.410e-01  7.443e-03  -18.95   <2e-16 ***
## NU_IDADE_N  -3.892e-02  9.946e-05 -391.31   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 2322208  on 1795884  degrees of freedom
## Residual deviance: 2140628  on 1795880  degrees of freedom
##   (335755 observations deleted due to missingness)
## AIC: 2140638
## 
## Number of Fisher Scoring iterations: 3

Optimizando o código

rm(casos_fil_covid_dem)
gc()

##             used   (Mb) gc trigger   (Mb)  max used   (Mb)
## Ncells   3337778  178.3    7418824  396.3   3390814  181.1
## Vcells 247414861 1887.7  632284791 4824.0 660739455 5041.1

Efeito vacina, regresão logit

casos_fil_covid_vacina <- casos_fil_covid %>% select(DT_MIN, VACINA_COV, VACINA, NU_IDADE_N, EVOLUCAO)

Summary do df vacina

summary(casos_fil_covid_vacina)

##     DT_MIN.DT_MIN       VACINA_COV         VACINA         NU_IDADE_N   
##  Min.   :2020-01-05   Min.   :1.0      Min.   :1.0      Min.   : 1.00  
##  1st Qu.:2020-11-05   1st Qu.:2.0      1st Qu.:2.0      1st Qu.:45.00  
##  Median :2021-03-21   Median :2.0      Median :2.0      Median :59.00  
##  Mean   :2021-03-12   Mean   :2.6      Mean   :5.1      Mean   :57.91  
##  3rd Qu.:2021-06-10   3rd Qu.:2.0      3rd Qu.:9.0      3rd Qu.:72.00  
##  Max.   :2022-12-04   Max.   :9.0      Max.   :9.0      Max.   :99.00  
##                       NA's   :325989   NA's   :526559                  
##     EVOLUCAO    
##  Min.   :1.0    
##  1st Qu.:1.0    
##  Median :1.0    
##  Mean   :1.5    
##  3rd Qu.:2.0    
##  Max.   :9.0    
##  NA's   :99271

Estruturando em one hot encoded para poder fazer a regressão logit.

casos_fil_covid_vacina <- dummy_cols(casos_fil_covid_vacina, select_columns = c('VACINA_COV', 'VACINA'),
           remove_selected_columns = TRUE)

Transformando a variável EVOLUCAO em dummy, 1 = sobreviveu, 0 = óbito.

casos_fil_covid_vacina$EVOLUCAO <- if_else(casos_fil_covid_vacina$EVOLUCAO == 1 , 1, 0)

Porcentagem de nulos na coluna EVOLUCAO.

sum(is.na(casos_fil_covid_vacina$EVOLUCAO)) / length(casos_fil_covid_vacina$EVOLUCAO)

## [1] 0.04657025

Variáveis selecionadas: CS_SEX_F = se é mulher ou não, CS_RACA_2 = se é preto ou não, CS_ZONA_2 = se é rural ou não, NU_IDADE_N = idade da pessoa. EVOLUCAO = 1 para sobreviveu, 0 para óbito. Recordo também que o comando glm omite as linhas com valores nulos.

casos_fil_covid.logit <- glm(formula = EVOLUCAO ~   VACINA_COV_1 + VACINA_1 + NU_IDADE_N, family = binomial(link = "logit"), 
    data = casos_fil_covid_vacina)
summary(casos_fil_covid.logit)

## 
## Call:
## glm(formula = EVOLUCAO ~ VACINA_COV_1 + VACINA_1 + NU_IDADE_N, 
##     family = binomial(link = "logit"), data = casos_fil_covid_vacina)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -2.7335  -1.1587   0.6586   0.9106   1.6606  
## 
## Coefficients:
##                Estimate Std. Error z value Pr(>|z|)    
## (Intercept)   3.0475995  0.0074691  408.03   <2e-16 ***
## VACINA_COV_1  0.3423209  0.0047937   71.41   <2e-16 ***
## VACINA_1      0.3637069  0.0057864   62.85   <2e-16 ***
## NU_IDADE_N   -0.0417804  0.0001196 -349.27   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 1755431  on 1364590  degrees of freedom
## Residual deviance: 1610957  on 1364587  degrees of freedom
##   (767049 observations deleted due to missingness)
## AIC: 1610965
## 
## Number of Fisher Scoring iterations: 3

Análise dos sintomas

rm(casos_fil_covid_vacina)
gc()

##             used   (Mb) gc trigger   (Mb)  max used   (Mb)
## Ncells   2907351  155.3    7418824  396.3   3545382  189.4
## Vcells 210745087 1607.9  632284791 4824.0 660739455 5041.1

casos_fil_covid_sintomas <- casos_fil_covid %>% select(DT_MIN, NU_IDADE_N, EVOLUCAO,FEBRE, TOSSE, GARGANTA, DISPNEIA, DESC_RESP, SATURACAO, DIARREIA, VOMITO, DOR_ABD, FADIGA, PERD_OLFT, PERD_PALA)
summary(casos_fil_covid_sintomas)

##     DT_MIN.DT_MIN       NU_IDADE_N       EVOLUCAO         FEBRE       
##  Min.   :2020-01-05   Min.   : 1.00   Min.   :1.0     Min.   :1.0     
##  1st Qu.:2020-11-05   1st Qu.:45.00   1st Qu.:1.0     1st Qu.:1.0     
##  Median :2021-03-21   Median :59.00   Median :1.0     Median :1.0     
##  Mean   :2021-03-12   Mean   :57.91   Mean   :1.5     Mean   :1.4     
##  3rd Qu.:2021-06-10   3rd Qu.:72.00   3rd Qu.:2.0     3rd Qu.:2.0     
##  Max.   :2022-12-04   Max.   :99.00   Max.   :9.0     Max.   :9.0     
##                                       NA's   :99271   NA's   :340351  
##      TOSSE           GARGANTA         DISPNEIA        DESC_RESP     
##  Min.   :1.0      Min.   :1        Min.   :1.00     Min.   :1.0     
##  1st Qu.:1.0      1st Qu.:2        1st Qu.:1.00     1st Qu.:1.0     
##  Median :1.0      Median :2        Median :1.00     Median :1.0     
##  Mean   :1.3      Mean   :2        Mean   :1.28     Mean   :1.4     
##  3rd Qu.:1.0      3rd Qu.:2        3rd Qu.:1.00     3rd Qu.:2.0     
##  Max.   :9.0      Max.   :9        Max.   :9.00     Max.   :9.0     
##  NA's   :268697   NA's   :615677   NA's   :265435   NA's   :409211  
##    SATURACAO         DIARREIA          VOMITO          DOR_ABD      
##  Min.   :1.0      Min.   :1        Min.   :1.0      Min.   :1.0     
##  1st Qu.:1.0      1st Qu.:2        1st Qu.:2.0      1st Qu.:2.0     
##  Median :1.0      Median :2        Median :2.0      Median :2.0     
##  Mean   :1.4      Mean   :2        Mean   :2.1      Mean   :2.2     
##  3rd Qu.:2.0      3rd Qu.:2        3rd Qu.:2.0      3rd Qu.:2.0     
##  Max.   :9.0      Max.   :9        Max.   :9.0      Max.   :9.0     
##  NA's   :341842   NA's   :643986   NA's   :671957   NA's   :873571  
##      FADIGA         PERD_OLFT        PERD_PALA     
##  Min.   :1.0      Min.   :1.0      Min.   :1.0     
##  1st Qu.:1.0      1st Qu.:2.0      1st Qu.:2.0     
##  Median :2.0      Median :2.0      Median :2.0     
##  Mean   :1.9      Mean   :2.1      Mean   :2.1     
##  3rd Qu.:2.0      3rd Qu.:2.0      3rd Qu.:2.0     
##  Max.   :9.0      Max.   :9.0      Max.   :9.0     
##  NA's   :796163   NA's   :852465   NA's   :853816

Criando as dummies

casos_fil_covid_sintomas <- dummy_cols(casos_fil_covid_sintomas, select_columns = c("FEBRE" , "TOSSE", "GARGANTA", "DISPNEIA", "DESC_RESP", "SATURACAO",  "DIARREIA", "VOMITO", "DOR_ABD",  "FADIGA", "PERD_OLFT", "PERD_PALA" ),
           remove_selected_columns = TRUE)

Transformando a variável EVOLUCAO em dummy, 1 = sobreviveu, 0 = óbito.

casos_fil_covid_sintomas$EVOLUCAO <- if_else(casos_fil_covid_sintomas$EVOLUCAO == 1 , 1, 0)

Logit dos sintomas

casos_fil_covid.logit <- glm(formula = EVOLUCAO ~  FEBRE_1 + TOSSE_1 + GARGANTA_1 + DISPNEIA_1,
           family = binomial(link = "logit"), 
    data = casos_fil_covid_sintomas)
summary(casos_fil_covid.logit)

## 
## Call:
## glm(formula = EVOLUCAO ~ FEBRE_1 + TOSSE_1 + GARGANTA_1 + DISPNEIA_1, 
##     family = binomial(link = "logit"), data = casos_fil_covid_sintomas)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -1.7954  -1.3919   0.8600   0.9164   1.1066  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept)  0.753840   0.004675  161.24   <2e-16 ***
## FEBRE_1      0.159034   0.003825   41.58   <2e-16 ***
## TOSSE_1      0.322382   0.004182   77.09   <2e-16 ***
## GARGANTA_1   0.153917   0.004632   33.23   <2e-16 ***
## DISPNEIA_1  -0.584917   0.004365 -134.00   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 1786226  on 1394858  degrees of freedom
## Residual deviance: 1758084  on 1394854  degrees of freedom
##   (736781 observations deleted due to missingness)
## AIC: 1758094
## 
## Number of Fisher Scoring iterations: 4

Tenho de separar em duas regressões, pois o R não imprime todos os valores, tem um limite a analisar.

casos_fil_covid.logit <- glm(formula = EVOLUCAO ~ DESC_RESP_1 + SATURACAO_1 + DIARREIA_1 + VOMITO_1 + DOR_ABD_1 +  FADIGA_1 + PERD_OLFT_1 + PERD_PALA_1,
           family = binomial(link = "logit"), 
    data = casos_fil_covid_sintomas)
summary(casos_fil_covid.logit)

## 
## Call:
## glm(formula = EVOLUCAO ~ DESC_RESP_1 + SATURACAO_1 + DIARREIA_1 + 
##     VOMITO_1 + DOR_ABD_1 + FADIGA_1 + PERD_OLFT_1 + PERD_PALA_1, 
##     family = binomial(link = "logit"), data = casos_fil_covid_sintomas)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -2.0685  -1.3002   0.7509   0.9405   1.0602  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept)  1.121971   0.004279 262.218   <2e-16 ***
## DESC_RESP_1 -0.375743   0.004563 -82.343   <2e-16 ***
## SATURACAO_1 -0.462214   0.004832 -95.653   <2e-16 ***
## DIARREIA_1   0.169312   0.006165  27.464   <2e-16 ***
## VOMITO_1     0.105631   0.007476  14.130   <2e-16 ***
## DOR_ABD_1   -0.002028   0.008309  -0.244    0.807    
## FADIGA_1     0.133151   0.004603  28.929   <2e-16 ***
## PERD_OLFT_1  0.227462   0.010308  22.066   <2e-16 ***
## PERD_PALA_1  0.256625   0.010236  25.072   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 1483778  on 1161598  degrees of freedom
## Residual deviance: 1451780  on 1161590  degrees of freedom
##   (970041 observations deleted due to missingness)
## AIC: 1451798
## 
## Number of Fisher Scoring iterations: 4

Análise das comorbidades

rm(casos_fil_covid_sintomas)
gc()

##             used   (Mb) gc trigger   (Mb)  max used   (Mb)
## Ncells   2704277  144.5    7418824  396.3   3545382  189.4
## Vcells 258063290 1968.9  632612713 4826.5 660739455 5041.1

casos_fil_covid_comorbidades <- casos_fil_covid %>% select(DT_MIN, NU_IDADE_N, EVOLUCAO, FATOR_RISC, PUERPERA, CARDIOPATI, HEMATOLOGI, SIND_DOWN, HEPATICA, ASMA, DIABETES, NEUROLOGIC, PNEUMOPATI, IMUNODEPRE, RENAL, OBESIDADE)

summary(casos_fil_covid_comorbidades)

##     DT_MIN.DT_MIN       NU_IDADE_N       EVOLUCAO      FATOR_RISC       
##  Min.   :2020-01-05   Min.   : 1.00   Min.   :1.0     Length:2131640    
##  1st Qu.:2020-11-05   1st Qu.:45.00   1st Qu.:1.0     Class :character  
##  Median :2021-03-21   Median :59.00   Median :1.0     Mode  :character  
##  Mean   :2021-03-12   Mean   :57.91   Mean   :1.5                       
##  3rd Qu.:2021-06-10   3rd Qu.:72.00   3rd Qu.:2.0                       
##  Max.   :2022-12-04   Max.   :99.00   Max.   :9.0                       
##                                       NA's   :99271                     
##     PUERPERA         CARDIOPATI        HEMATOLOGI        SIND_DOWN      
##  Min.   :1.0       Min.   :1.0       Min.   :1.0       Min.   :1.0      
##  1st Qu.:2.0       1st Qu.:1.0       1st Qu.:2.0       1st Qu.:2.0      
##  Median :2.0       Median :1.0       Median :2.0       Median :2.0      
##  Mean   :2.2       Mean   :1.4       Mean   :2.2       Mean   :2.2      
##  3rd Qu.:2.0       3rd Qu.:2.0       3rd Qu.:2.0       3rd Qu.:2.0      
##  Max.   :9.0       Max.   :9.0       Max.   :9.0       Max.   :9.0      
##  NA's   :1352981   NA's   :1099368   NA's   :1350649   NA's   :1353343  
##     HEPATICA            ASMA            DIABETES         NEUROLOGIC     
##  Min.   :1.0       Min.   :1.0       Min.   :1.0       Min.   :1.0      
##  1st Qu.:2.0       1st Qu.:2.0       1st Qu.:1.0       1st Qu.:2.0      
##  Median :2.0       Median :2.0       Median :2.0       Median :2.0      
##  Mean   :2.2       Mean   :2.1       Mean   :1.6       Mean   :2.1      
##  3rd Qu.:2.0       3rd Qu.:2.0       3rd Qu.:2.0       3rd Qu.:2.0      
##  Max.   :9.0       Max.   :9.0       Max.   :9.0       Max.   :9.0      
##  NA's   :1352960   NA's   :1340898   NA's   :1172542   NA's   :1331091  
##    PNEUMOPATI        IMUNODEPRE          RENAL           OBESIDADE      
##  Min.   :1.0       Min.   :1.0       Min.   :1.0       Min.   :1        
##  1st Qu.:2.0       1st Qu.:2.0       1st Qu.:2.0       1st Qu.:2        
##  Median :2.0       Median :2.0       Median :2.0       Median :2        
##  Mean   :2.1       Mean   :2.1       Mean   :2.1       Mean   :2        
##  3rd Qu.:2.0       3rd Qu.:2.0       3rd Qu.:2.0       3rd Qu.:2        
##  Max.   :9.0       Max.   :9.0       Max.   :9.0       Max.   :9        
##  NA's   :1332442   NA's   :1342756   NA's   :1334452   NA's   :1303592

Criando as dummies

casos_fil_covid_comorbidades <- dummy_cols(casos_fil_covid_comorbidades, select_columns = c('FATOR_RISC', 'PUERPERA', 'CARDIOPATI', 'HEMATOLOGI', 'SIND_DOWN', 'HEPATICA', 'ASMA', 'DIABETES', 'NEUROLOGIC', 'PNEUMOPATI', 'IMUNODEPRE', 'RENAL', 'OBESIDADE'),
           remove_selected_columns = TRUE)

casos_fil_covid_comorbidades$EVOLUCAO <- if_else(casos_fil_covid_comorbidades$EVOLUCAO == 1 , 1, 0)

summary(casos_fil_covid_comorbidades)

##      DT_MIN             NU_IDADE_N       EVOLUCAO      FATOR_RISC_1   
##  Min.   :2020-01-05   Min.   : 1.00   Min.   :0.00    Min.   :0.0000  
##  1st Qu.:2020-11-05   1st Qu.:45.00   1st Qu.:0.00    1st Qu.:0.0000  
##  Median :2021-03-21   Median :59.00   Median :1.00    Median :0.0000  
##  Mean   :2021-03-12   Mean   :57.91   Mean   :0.65    Mean   :0.3899  
##  3rd Qu.:2021-06-10   3rd Qu.:72.00   3rd Qu.:1.00    3rd Qu.:1.0000  
##  Max.   :2022-12-04   Max.   :99.00   Max.   :1.00    Max.   :1.0000  
##                                       NA's   :99271                   
##   FATOR_RISC_2     FATOR_RISC_N     FATOR_RISC_S      PUERPERA_1     
##  Min.   :0.0000   Min.   :0.0000   Min.   :0.0000   Min.   :0        
##  1st Qu.:0.0000   1st Qu.:0.0000   1st Qu.:0.0000   1st Qu.:0        
##  Median :0.0000   Median :0.0000   Median :0.0000   Median :0        
##  Mean   :0.2743   Mean   :0.1229   Mean   :0.2128   Mean   :0        
##  3rd Qu.:1.0000   3rd Qu.:0.0000   3rd Qu.:0.0000   3rd Qu.:0        
##  Max.   :1.0000   Max.   :1.0000   Max.   :1.0000   Max.   :1        
##                                                     NA's   :1352981  
##    PUERPERA_2        PUERPERA_9       PUERPERA_NA      CARDIOPATI_1    
##  Min.   :0         Min.   :0         Min.   :0.0000   Min.   :0.0      
##  1st Qu.:1         1st Qu.:0         1st Qu.:0.0000   1st Qu.:0.0      
##  Median :1         Median :0         Median :1.0000   Median :1.0      
##  Mean   :1         Mean   :0         Mean   :0.6347   Mean   :0.6      
##  3rd Qu.:1         3rd Qu.:0         3rd Qu.:1.0000   3rd Qu.:1.0      
##  Max.   :1         Max.   :1         Max.   :1.0000   Max.   :1.0      
##  NA's   :1352981   NA's   :1352981                    NA's   :1099368  
##   CARDIOPATI_2      CARDIOPATI_9     CARDIOPATI_NA     HEMATOLOGI_1    
##  Min.   :0.0       Min.   :0         Min.   :0.0000   Min.   :0        
##  1st Qu.:0.0       1st Qu.:0         1st Qu.:0.0000   1st Qu.:0        
##  Median :0.0       Median :0         Median :1.0000   Median :0        
##  Mean   :0.3       Mean   :0         Mean   :0.5157   Mean   :0        
##  3rd Qu.:1.0       3rd Qu.:0         3rd Qu.:1.0000   3rd Qu.:0        
##  Max.   :1.0       Max.   :1         Max.   :1.0000   Max.   :1        
##  NA's   :1099368   NA's   :1099368                    NA's   :1350649  
##   HEMATOLOGI_2      HEMATOLOGI_9     HEMATOLOGI_NA     SIND_DOWN_1     
##  Min.   :0         Min.   :0         Min.   :0.0000   Min.   :0        
##  1st Qu.:1         1st Qu.:0         1st Qu.:0.0000   1st Qu.:0        
##  Median :1         Median :0         Median :1.0000   Median :0        
##  Mean   :1         Mean   :0         Mean   :0.6336   Mean   :0        
##  3rd Qu.:1         3rd Qu.:0         3rd Qu.:1.0000   3rd Qu.:0        
##  Max.   :1         Max.   :1         Max.   :1.0000   Max.   :1        
##  NA's   :1350649   NA's   :1350649                    NA's   :1353343  
##   SIND_DOWN_2       SIND_DOWN_9       SIND_DOWN_NA      HEPATICA_1     
##  Min.   :0         Min.   :0         Min.   :0.0000   Min.   :0        
##  1st Qu.:1         1st Qu.:0         1st Qu.:0.0000   1st Qu.:0        
##  Median :1         Median :0         Median :1.0000   Median :0        
##  Mean   :1         Mean   :0         Mean   :0.6349   Mean   :0        
##  3rd Qu.:1         3rd Qu.:0         3rd Qu.:1.0000   3rd Qu.:0        
##  Max.   :1         Max.   :1         Max.   :1.0000   Max.   :1        
##  NA's   :1353343   NA's   :1353343                    NA's   :1352960  
##    HEPATICA_2        HEPATICA_9       HEPATICA_NA         ASMA_1       
##  Min.   :0         Min.   :0         Min.   :0.0000   Min.   :0.0      
##  1st Qu.:1         1st Qu.:0         1st Qu.:0.0000   1st Qu.:0.0      
##  Median :1         Median :0         Median :1.0000   Median :0.0      
##  Mean   :1         Mean   :0         Mean   :0.6347   Mean   :0.1      
##  3rd Qu.:1         3rd Qu.:0         3rd Qu.:1.0000   3rd Qu.:0.0      
##  Max.   :1         Max.   :1         Max.   :1.0000   Max.   :1.0      
##  NA's   :1352960   NA's   :1352960                    NA's   :1340898  
##      ASMA_2            ASMA_9           ASMA_NA        DIABETES_1     
##  Min.   :0.0       Min.   :0         Min.   :0.000   Min.   :0.0      
##  1st Qu.:1.0       1st Qu.:0         1st Qu.:0.000   1st Qu.:0.0      
##  Median :1.0       Median :0         Median :1.000   Median :0.0      
##  Mean   :0.9       Mean   :0         Mean   :0.629   Mean   :0.5      
##  3rd Qu.:1.0       3rd Qu.:0         3rd Qu.:1.000   3rd Qu.:1.0      
##  Max.   :1.0       Max.   :1         Max.   :1.000   Max.   :1.0      
##  NA's   :1340898   NA's   :1340898                   NA's   :1172542  
##    DIABETES_2        DIABETES_9       DIABETES_NA      NEUROLOGIC_1    
##  Min.   :0.0       Min.   :0         Min.   :0.0000   Min.   :0.0      
##  1st Qu.:0.0       1st Qu.:0         1st Qu.:0.0000   1st Qu.:0.0      
##  Median :0.0       Median :0         Median :1.0000   Median :0.0      
##  Mean   :0.5       Mean   :0         Mean   :0.5501   Mean   :0.1      
##  3rd Qu.:1.0       3rd Qu.:0         3rd Qu.:1.0000   3rd Qu.:0.0      
##  Max.   :1.0       Max.   :1         Max.   :1.0000   Max.   :1.0      
##  NA's   :1172542   NA's   :1172542                    NA's   :1331091  
##   NEUROLOGIC_2      NEUROLOGIC_9     NEUROLOGIC_NA     PNEUMOPATI_1    
##  Min.   :0.0       Min.   :0         Min.   :0.0000   Min.   :0.0      
##  1st Qu.:1.0       1st Qu.:0         1st Qu.:0.0000   1st Qu.:0.0      
##  Median :1.0       Median :0         Median :1.0000   Median :0.0      
##  Mean   :0.9       Mean   :0         Mean   :0.6244   Mean   :0.1      
##  3rd Qu.:1.0       3rd Qu.:0         3rd Qu.:1.0000   3rd Qu.:0.0      
##  Max.   :1.0       Max.   :1         Max.   :1.0000   Max.   :1.0      
##  NA's   :1331091   NA's   :1331091                    NA's   :1332442  
##   PNEUMOPATI_2      PNEUMOPATI_9     PNEUMOPATI_NA     IMUNODEPRE_1    
##  Min.   :0.0       Min.   :0         Min.   :0.0000   Min.   :0.0      
##  1st Qu.:1.0       1st Qu.:0         1st Qu.:0.0000   1st Qu.:0.0      
##  Median :1.0       Median :0         Median :1.0000   Median :0.0      
##  Mean   :0.9       Mean   :0         Mean   :0.6251   Mean   :0.1      
##  3rd Qu.:1.0       3rd Qu.:0         3rd Qu.:1.0000   3rd Qu.:0.0      
##  Max.   :1.0       Max.   :1         Max.   :1.0000   Max.   :1.0      
##  NA's   :1332442   NA's   :1332442                    NA's   :1342756  
##   IMUNODEPRE_2      IMUNODEPRE_9     IMUNODEPRE_NA       RENAL_1       
##  Min.   :0.0       Min.   :0         Min.   :0.0000   Min.   :0.0      
##  1st Qu.:1.0       1st Qu.:0         1st Qu.:0.0000   1st Qu.:0.0      
##  Median :1.0       Median :0         Median :1.0000   Median :0.0      
##  Mean   :0.9       Mean   :0         Mean   :0.6299   Mean   :0.1      
##  3rd Qu.:1.0       3rd Qu.:0         3rd Qu.:1.0000   3rd Qu.:0.0      
##  Max.   :1.0       Max.   :1         Max.   :1.0000   Max.   :1.0      
##  NA's   :1342756   NA's   :1342756                    NA's   :1334452  
##     RENAL_2           RENAL_9           RENAL_NA      OBESIDADE_1     
##  Min.   :0.0       Min.   :0         Min.   :0.000   Min.   :0.0      
##  1st Qu.:1.0       1st Qu.:0         1st Qu.:0.000   1st Qu.:0.0      
##  Median :1.0       Median :0         Median :1.000   Median :0.0      
##  Mean   :0.9       Mean   :0         Mean   :0.626   Mean   :0.2      
##  3rd Qu.:1.0       3rd Qu.:0         3rd Qu.:1.000   3rd Qu.:0.0      
##  Max.   :1.0       Max.   :1         Max.   :1.000   Max.   :1.0      
##  NA's   :1334452   NA's   :1334452                   NA's   :1303592  
##   OBESIDADE_2       OBESIDADE_9       OBESIDADE_NA   
##  Min.   :0.0       Min.   :0         Min.   :0.0000  
##  1st Qu.:1.0       1st Qu.:0         1st Qu.:0.0000  
##  Median :1.0       Median :0         Median :1.0000  
##  Mean   :0.8       Mean   :0         Mean   :0.6115  
##  3rd Qu.:1.0       3rd Qu.:0         3rd Qu.:1.0000  
##  Max.   :1.0       Max.   :1         Max.   :1.0000  
##  NA's   :1303592   NA's   :1303592

Logit comorbidades

casos_fil_covid.logit <- glm(formula = EVOLUCAO ~ FATOR_RISC_1 + PUERPERA_1 + CARDIOPATI_1 + HEMATOLOGI_1 + SIND_DOWN_1 + HEPATICA_1 + ASMA_1 + DIABETES_1 + NEUROLOGIC_1 + PNEUMOPATI_1 + IMUNODEPRE_1 + RENAL_1 + OBESIDADE_1 ,
           family = binomial(link = "logit"), 
    data = casos_fil_covid_comorbidades)
summary(casos_fil_covid.logit)

## 
## Call:
## glm(formula = EVOLUCAO ~ FATOR_RISC_1 + PUERPERA_1 + CARDIOPATI_1 + 
##     HEMATOLOGI_1 + SIND_DOWN_1 + HEPATICA_1 + ASMA_1 + DIABETES_1 + 
##     NEUROLOGIC_1 + PNEUMOPATI_1 + IMUNODEPRE_1 + RENAL_1 + OBESIDADE_1, 
##     family = binomial(link = "logit"), data = casos_fil_covid_comorbidades)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -2.0496  -1.3077   0.8999   0.9911   2.2419  
## 
## Coefficients:
##               Estimate Std. Error z value Pr(>|z|)    
## (Intercept)   0.761552   0.005652 134.747  < 2e-16 ***
## FATOR_RISC_1 -0.066832   0.005127 -13.035  < 2e-16 ***
## PUERPERA_1    0.777301   0.041216  18.859  < 2e-16 ***
## CARDIOPATI_1 -0.221110   0.004977 -44.429  < 2e-16 ***
## HEMATOLOGI_1 -0.110694   0.022751  -4.865 1.14e-06 ***
## SIND_DOWN_1  -0.002804   0.034751  -0.081    0.936    
## HEPATICA_1   -0.447586   0.021010 -21.304  < 2e-16 ***
## ASMA_1        0.431097   0.013041  33.056  < 2e-16 ***
## DIABETES_1   -0.239192   0.005109 -46.820  < 2e-16 ***
## NEUROLOGIC_1 -0.564989   0.009985 -56.585  < 2e-16 ***
## PNEUMOPATI_1 -0.502164   0.010495 -47.850  < 2e-16 ***
## IMUNODEPRE_1 -0.404165   0.012380 -32.647  < 2e-16 ***
## RENAL_1      -0.591436   0.010353 -57.130  < 2e-16 ***
## OBESIDADE_1  -0.041856   0.007138  -5.863 4.53e-09 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 954775  on 708893  degrees of freedom
## Residual deviance: 937544  on 708880  degrees of freedom
##   (1422746 observations deleted due to missingness)
## AIC: 937572
## 
## Number of Fisher Scoring iterations: 4

Projeto covid1 regressão

Thomas Daher

30/01/2023

Instalação de pacotes e extração de dados

Packages

Library

Criando tabela casos

Filtrando somente casos de covid

Criando a coluna menor data

Summary do df

Deixando nascimento em formato de data para o R e analisando a coluna NU_IDADE_N. Podemos ver que algumas pessoas possuem idades não condizentes com a realidade, com idades menores do que 0 ou muito acima da pessoa mais velha registrada no Brasil, portanto limito a 100 anos de idade.

Criando tabelas por tipo (Possuimos muitas colunas, algumas delas com muitos nulos, portanto para verificar quais possuem impacto na mortalidade vou separar por tipo, por exemplo, demográficas, comorbidades, relacionadas a vacinação, etc.)

Demográficas e economicas

Summary do df demográfico

Estruturando em one hot encoded para poder fazer a regressão logit.

Transformando a variável EVOLUCAO em dummy, 1 = sobreviveu, 0 = óbito.

Porcentagem de nulos na coluna EVOLUCAO.

Variáveis selecionadas: CS_SEX_F = se é mulher ou não, CS_RACA_2 = se é preto ou não, CS_ZONA_2 = se é rural ou não, NU_IDADE_N = idade da pessoa. EVOLUCAO = 1 para sobreviveu, 0 para óbito. Recordo também que o comando glm omite as linhas com valores nulos.

Optimizando o código

Efeito vacina, regresão logit

Summary do df vacina

Estruturando em one hot encoded para poder fazer a regressão logit.

Transformando a variável EVOLUCAO em dummy, 1 = sobreviveu, 0 = óbito.

Porcentagem de nulos na coluna EVOLUCAO.

Variáveis selecionadas: CS_SEX_F = se é mulher ou não, CS_RACA_2 = se é preto ou não, CS_ZONA_2 = se é rural ou não, NU_IDADE_N = idade da pessoa. EVOLUCAO = 1 para sobreviveu, 0 para óbito. Recordo também que o comando glm omite as linhas com valores nulos.

Análise dos sintomas

Criando as dummies

Transformando a variável EVOLUCAO em dummy, 1 = sobreviveu, 0 = óbito.

Logit dos sintomas

Tenho de separar em duas regressões, pois o R não imprime todos os valores, tem um limite a analisar.

Análise das comorbidades

Criando as dummies

Logit comorbidades