Calidad reportes Trauma Dental

Packages

pacman::p_load(tidyverse, 
               janitor, 
               visdat, 
               tableone, 
               emmeans)

Dataset

df <- read_csv("https://docs.google.com/spreadsheets/d/e/2PACX-1vSlA8QaLV8oP976nOJ0l9RwdZRqnPY5RXyEFFyXwWQ3iEZEc_xKVynZZGcXjAWLveK1ckDiOBTnwy2V/pub?gid=888653489&single=true&output=csv")

Parsed with column specification:
cols(
  .default = col_character(),
  `Número de autores` = col_double(),
  `Año de publicación` = col_double(),
  `n secuelas` = col_double(),
  `Pacientes que incluye el informe` = col_double(),
  Cumple = col_double(),
  `No cumple` = col_double(),
  SUMA = col_double()
)
See spec(...) for full column specifications.

Data exploration 1

head(df)

# names(df)

Data cleaning

Cleaning and removing not used variables

df <- df %>% 
  janitor::clean_names() %>% 
  janitor::remove_empty(c("cols", "rows"))

df <- df %>%
  select(
    titulo_del_estudio, 
  revista,
  pais_1er_autor_o_afiliacion,
  continente,
  numero_de_autores,
  ano_de_publicacion,
  antes_post_care,
  denticion_afect,
  tema,
  t1:ci13, 
  calidad = cumple
  )

Check NA

visdat::vis_dat(df)

Remove columns with more than 50% of NA

df <- df %>% 
  purrr::discard(~sum(is.na(.x))/length(.x)* 100 >=50) 

Elimino los que tienen revista o año NA

Dataset ready for analysis

Analysis and tables

Year and journals

df %>% 
  ggplot(aes(x = ano_de_publicacion, fill = revista)) + 
  geom_histogram(bins = 10) + 
  labs(
    title = "Articles per journal per year", 
    x = "Year", 
    y = "Articles", 
    fill = "Journal"
  ) + 
  ggpubr::theme_pubclean()

Table journal by year by n

df %>%
  group_by(revista, ano_de_publicacion) %>% 
  summarise(n = n()) %>% 
  pivot_wider(names_from = ano_de_publicacion, 
              values_from = n, 
              values_fill = list(n = 0))

Graph journal by n by year

df %>%
  group_by(revista, ano_de_publicacion) %>% 
  summarise(n = n()) %>% 
  ggplot(aes(x = as_factor(ano_de_publicacion), 
             y = n, 
             group = revista, 
             color = revista)) + 
  geom_line() + 
  scale_y_log10() + 
  labs(
    title = "Publication by journal", 
    x = "Year", 
    y = "Articles (Log 10 n)", 
    color = "Journal"
  )  + 
  ggpubr::theme_pubclean()

df %>% 
  janitor::tabyl(revista) %>% 
  adorn_totals("row") %>%
  adorn_pct_formatting()

                 revista   n percent
          Dent Traumatol 141   69.5%
  Eur Arch Paediatr Dent  10    4.9%
 Int J Clin Pediatr Dent  16    7.9%
         Int J Paed Dent   2    1.0%
     J Clin Pediatr Dent  19    9.4%
            Pediatr Dent  15    7.4%
                   Total 203  100.0%

df %>%
  janitor::tabyl(ano_de_publicacion, revista) %>%
  janitor::adorn_percentages("col") %>%
  janitor::adorn_totals("col") %>% 
  janitor::adorn_pct_formatting(rounding = "half up", digits = 0) %>%
  janitor::adorn_ns() %>% 
  janitor::adorn_title("combined") 

 ano_de_publicacion/revista Dent Traumatol Eur Arch Paediatr Dent Int J Clin Pediatr Dent
                       2008       35% (50)                10% (1)                  0% (0)
                       2009       12% (17)                10% (1)                 13% (2)
                       2010       17% (24)                 0% (0)                  0% (0)
                       2011       17% (24)                30% (3)                 19% (3)
                       2012        7% (10)                20% (2)                 13% (2)
                       2013        3%  (4)                 0% (0)                 19% (3)
                       2014        2%  (3)                 0% (0)                  6% (1)
                       2015        3%  (4)                10% (1)                 13% (2)
                       2016        1%  (1)                 0% (0)                  6% (1)
                       2017        1%  (2)                 0% (0)                  6% (1)
                       2018        1%  (2)                20% (2)                  6% (1)
 Int J Paed Dent J Clin Pediatr Dent Pediatr Dent     Total
          0% (0)              0% (0)       0% (0)  45% (51)
          0% (0)             11% (2)      33% (5)  78% (27)
          0% (0)             11% (2)      20% (3)  48% (29)
          0% (0)              5% (1)       7% (1)  78% (32)
         50% (1)             21% (4)      33% (5) 144% (24)
          0% (0)             11% (2)       0% (0)  32%  (9)
         50% (1)              0% (0)       0% (0)  58%  (5)
          0% (0)             11% (2)       7% (1)  43% (10)
          0% (0)             16% (3)       0% (0)  23%  (5)
          0% (0)             16% (3)       0% (0)  23%  (6)
          0% (0)              0% (0)       0% (0)  28%  (5)

Countries, journals and year

Countries

df %>%
  janitor::tabyl(pais_1er_autor_o_afiliacion) %>%
  arrange(desc(n)) %>% 
  #  adorn_totals("row") %>%
  adorn_pct_formatting(rounding = "half up", digits = 0)

Countries per year

df %>% 
  group_by(pais_1er_autor_o_afiliacion, ano_de_publicacion, revista) %>%
  count(sort = T) %>% 
  filter(n > 1) %>% 
  ggplot(aes(x = as_factor(ano_de_publicacion), y = n, color = pais_1er_autor_o_afiliacion)) + 
  geom_jitter(alpha = .7) + 
  labs(
    title = "Case reports by country",
    subtitle = "Only countries with >1", 
    x = "Year", 
    y = "Case reports", 
    color = "Country"
  ) +
  ggpubr::theme_pubclean()

  


pivot_wider(names_from = ano_de_publicacion, values_from = n) %>% 
  ggplot(aes(x = as_factor(ano_de_publicacion), y = n))

Error in build_wider_spec(data, names_from = !!names_from, values_from = !!values_from,  : 
  el argumento "data" está ausente, sin valor por omisión

Pre and post CARE

df %>% 
  aov(calidad ~ antes_post_care, data = .) %>% 
  summary()

                 Df Sum Sq Mean Sq F value Pr(>F)
antes_post_care   1    5.1   5.076   0.735  0.392
Residuals       201 1388.2   6.907               

Pre and post CARE by country

Pendiente

ver si existe cambio por país antes y despues

Ver distribución por paises años revistas

df %>%
  janitor::tabyl(revista) %>%
  arrange(desc(n)) %>% 
  #  adorn_totals("row") %>%
  adorn_pct_formatting(rounding = "half up", digits = 0) %>% 
  adorn_totals("col")

hacer una tabla que combine todo esto

luego hacer una evaluación de la calidad

Quality

Calculo la calidad total

df <- df %>%
  mutate(calidad = reduce(select( .,  introduccion_4_b_hace_referencia_a_la_literatura_medica_pertinente_number:x88),
  `+`  ))

mean(df$calidad)

[1] 14.36946

Quality by year

Quality by journal

Figure quality mean by year and journal

Inferential

Hay diferencia por año?

summary(aov(calidad ~ ano_de_publicacion, data = df))

                    Df Sum Sq Mean Sq F value  Pr(>F)   
ano_de_publicacion   1   45.9   45.89   6.846 0.00956 **
Residuals          201 1347.4    6.70                   
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Hay diferencia por revista?

summary(aov(calidad ~ revista, data = df))

             Df Sum Sq Mean Sq F value  Pr(>F)   
revista       5  104.9   20.98   3.209 0.00829 **
Residuals   197 1288.4    6.54                   
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Hay diferencia antes y después del 2013?

df %>% 
  mutate(care = case_when(
    ano_de_publicacion > 2013 ~ "Despues", 
    TRUE ~ "Antes" 
  )) %>% 
  aov(calidad~care, data = .) %>% 
  summary()

             Df Sum Sq Mean Sq F value Pr(>F)
care          1    5.1   5.076   0.735  0.392
Residuals   201 1388.2   6.907               

df %>% 
  aov(calidad~revista, data = .) %>% 
  TukeyHSD()

  Tukey multiple comparisons of means
    95% family-wise confidence level

Fit: aov(formula = calidad ~ revista, data = .)

$revista
                                                     diff        lwr        upr     p adj
Eur Arch Paediatr Dent-Dent Traumatol           2.3666667 -0.0418685  4.7752018 0.0572236
Int J Clin Pediatr Dent-Dent Traumatol         -1.4583333 -3.3999091  0.4832425 0.2606121
Int J Paed Dent-Dent Traumatol                  2.1666667 -3.0743741  7.4077074 0.8413587
J Clin Pediatr Dent-Dent Traumatol             -0.2807018 -2.0793561  1.5179526 0.9976729
Pediatr Dent-Dent Traumatol                     0.5333333 -1.4655212  2.5321879 0.9725610
Int J Clin Pediatr Dent-Eur Arch Paediatr Dent -3.8250000 -6.7918853 -0.8581147 0.0036173
Int J Paed Dent-Eur Arch Paediatr Dent         -0.2000000 -5.9009824  5.5009824 0.9999985
J Clin Pediatr Dent-Eur Arch Paediatr Dent     -2.6473684 -5.5227552  0.2280184 0.0904329
Pediatr Dent-Eur Arch Paediatr Dent            -1.8333333 -4.8380149  1.1713482 0.4966393
Int J Paed Dent-Int J Clin Pediatr Dent         3.6250000 -1.8949525  9.1449525 0.4113784
J Clin Pediatr Dent-Int J Clin Pediatr Dent     1.1776316 -1.3196718  3.6749350 0.7524336
Pediatr Dent-Int J Clin Pediatr Dent            1.9916667 -0.6534772  4.6368105 0.2580469
J Clin Pediatr Dent-Int J Paed Dent            -2.4473684 -7.9186860  3.0239492 0.7916917
Pediatr Dent-Int J Paed Dent                   -1.6333333 -7.1736923  3.9070257 0.9578912
Pediatr Dent-J Clin Pediatr Dent                0.8140351 -1.7280560  3.3561261 0.9405930

Journal comparison

Year comparison

Continent comparison

Pre-post CARE comparison

first author country comparison