Knowledge, Attitudes, and Perceived Barriers Toward Pediatric Preventive Dentistry

Demetrio Data

1 Setup

1.1 Load packages

# Load all required packages via pacman
if (!requireNamespace("pacman", quietly = TRUE)) install.packages("pacman")
pacman::p_load(tidyverse, here, janitor, viridis, scales, MASS, gtsummary)

1.2 Read and clean data

# Read the raw CSV, normalize column names with janitor, then rename
# to short workable aliases. This is the only persistent object we keep
# thorughout the analysis
df <- read_csv(here("data", "data_2026_09_28.csv"),
         show_col_types = FALSE) |> 
  clean_names() |> 
  # Rename the long janitor names to short aliases
  rename(
    specialty      = dental_specialty,
    q1_sugar       = x1_what_is_the_maximum_recommended_sugar_consumption_during_the_day,
    q2_first_visit = x2_at_what_age_is_the_first_visit_recommended,
    q3_fluoride    = x3_which_of_the_following_statements_about_fluoride_is_correct,
    q4_sealants    = x4_which_of_the_following_questions_about_sealants_is_correct,
    q5_ecc         = x5_which_of_the_following_statements_about_early_childhood_caries_ecc_is_correct,
    q6_brushing    = x6_which_of_the_following_statements_about_brushing_is_correct,
    q7_risk        = x7_which_of_the_following_statements_about_oral_health_risk_factors_is_wrong,
    q8_smoking     = x8_dentists_should_carry_out_preventive_activities_for_smoking_cessation,
    q9_paed_prev   = x9_dentists_should_carry_out_preventive_activities_in_paediatric_dentistry,
    q10_equipped   = x10_dentists_are_well_equipped_in_carrying_out_preventive_care_in_paediatric_dentistry,
    q11_sealants   = x11_when_needed_do_you_apply_dental_sealants,
    q12_varnish    = x12_when_needed_do_you_apply_fluoride_varnish,
    q13_ohi        = x13_when_needed_do_you_give_oral_hygiene_instructions,
    q14_diet       = x14_when_needed_do_you_provide_any_dietary_advice,
    q15_time       = x15_lack_of_time,
    q16_remuneration = x16_lack_of_remuneration,
    q17_motivation = x17_lack_of_motivation_in_implementing_preventive_care,
    q18_compliance = x18_poor_patient_compliance,
    q19_knowledge_b = x19_lack_of_knowledge,
    q20_training   = x20_lack_of_training
  ) |> 
  # Recode categorical variables
  mutate(
    age_group = factor(age,
                       levels = 1:6,
                       labels = c("25-34", "35-44", "45-54",
                                  "55-64", "65-74", "75+")),
    gender_label = factor(gender,
                          levels = c(0, 1),
                          labels = c("Male", "Female")),
    specialty_label = factor(specialty,
                             levels = 1:7,
                             labels = c("General Dentistry", "Pediatric Dentistry",
                                        "Orthodontics", "Oral Surgery",
                                        "Restorative/Endo", "Prosthodontics",
                                        "Periodontics")),
    country = factor(country)
  ) -> df

2 Demographics

2.1 Demographics table (Table 1)

# Table 1: sample characteristics stratified by country using gtsummary
df |> 
  select(country, age_group, gender_label, specialty_label) |> 
  tbl_summary(
    by = country,
    label = list(
      age_group       ~ "Age group",
      gender_label    ~ "Gender",
      specialty_label ~ "Dental specialty"
    ),
    missing = "ifany",
    missing_text = "Missing"
  ) |> 
  add_overall() |> 
  add_p(test = list(all_categorical() ~ "chisq.test")) |> 
  modify_header(label ~ "**Characteristic**") |> 
  bold_labels()

Characteristic	Overall N = 3,5771	Argentina N = 3821	Chile N = 7481	India N = 3601	Italy N = 3221	Latvia N = 1001	Serbia N = 8451	Spain N = 7081	Switzerland N = 1121	p-value2
Age group										<0.001
25-34	1,098 (31%)	86 (23%)	280 (37%)	318 (88%)	51 (16%)	27 (27%)	183 (22%)	130 (18%)	23 (21%)
35-44	981 (27%)	82 (21%)	279 (37%)	28 (7.8%)	47 (15%)	33 (33%)	268 (32%)	208 (29%)	36 (32%)
45-54	749 (21%)	122 (32%)	106 (14%)	13 (3.6%)	56 (17%)	23 (23%)	232 (27%)	178 (25%)	19 (17%)
55-64	526 (15%)	73 (19%)	58 (7.8%)	1 (0.3%)	105 (33%)	16 (16%)	128 (15%)	118 (17%)	27 (24%)
65-74	201 (5.6%)	15 (3.9%)	24 (3.2%)	0 (0%)	58 (18%)	1 (1.0%)	31 (3.7%)	67 (9.5%)	5 (4.5%)
75+	22 (0.6%)	4 (1.0%)	1 (0.1%)	0 (0%)	5 (1.6%)	0 (0%)	3 (0.4%)	7 (1.0%)	2 (1.8%)
Gender										<0.001
Male	1,118 (31%)	77 (20%)	237 (32%)	108 (30%)	212 (66%)	5 (5.0%)	196 (23%)	236 (33%)	47 (43%)
Female	2,455 (69%)	305 (80%)	511 (68%)	252 (70%)	110 (34%)	95 (95%)	647 (77%)	472 (67%)	63 (57%)
Missing	4	0	0	0	0	0	2	0	2
Dental specialty										<0.001
General Dentistry	1,825 (51%)	172 (45%)	438 (59%)	147 (41%)	128 (40%)	82 (82%)	480 (57%)	350 (49%)	28 (25%)
Pediatric Dentistry	518 (14%)	98 (26%)	140 (19%)	19 (5.3%)	13 (4.0%)	11 (11%)	152 (18%)	64 (9.0%)	21 (19%)
Orthodontics	240 (6.7%)	32 (8.4%)	29 (3.9%)	9 (2.5%)	36 (11%)	1 (1.0%)	44 (5.2%)	75 (11%)	14 (13%)
Oral Surgery	262 (7.3%)	21 (5.5%)	25 (3.3%)	22 (6.1%)	39 (12%)	0 (0%)	55 (6.5%)	96 (14%)	4 (3.6%)
Restorative/Endo	414 (12%)	42 (11%)	34 (4.5%)	146 (41%)	45 (14%)	2 (2.0%)	40 (4.7%)	79 (11%)	26 (23%)
Prosthodontics	246 (6.9%)	12 (3.1%)	78 (10%)	9 (2.5%)	41 (13%)	2 (2.0%)	54 (6.4%)	38 (5.4%)	12 (11%)
Periodontics	72 (2.0%)	5 (1.3%)	4 (0.5%)	8 (2.2%)	20 (6.2%)	2 (2.0%)	20 (2.4%)	6 (0.8%)	7 (6.3%)
1 n (%)
2 Pearson’s Chi-squared test

3 Knowledge

3.1 Descriptives by country

# Knowledge score summary stratified by country
df |> 
  select(country, score_knowledge) |> 
  tbl_summary(
    by = country,
    label = list(score_knowledge ~ "Knowledge score (0-7)"),
    statistic = list(all_continuous() ~ "{mean} ({sd}) | {median} [{p25}, {p75}]"),
    missing = "no"
  ) |> 
  add_overall() |> 
  add_p(test = list(all_continuous() ~ "kruskal.test")) |> 
  bold_labels()

Characteristic	Overall N = 3,5771	Argentina N = 3821	Chile N = 7481	India N = 3601	Italy N = 3221	Latvia N = 1001	Serbia N = 8451	Spain N = 7081	Switzerland N = 1121	p-value2
Knowledge score (0-7)
0	7 (0.2%)	0 (0%)	0 (0%)	3 (0.8%)	0 (0%)	0 (0%)	0 (0%)	3 (0.4%)	1 (0.9%)
1	28 (0.8%)	1 (0.3%)	0 (0%)	19 (5.3%)	0 (0%)	1 (1.0%)	3 (0.4%)	1 (0.1%)	3 (2.7%)
2	68 (1.9%)	16 (4.2%)	1 (0.1%)	16 (4.4%)	8 (2.5%)	4 (4.0%)	8 (0.9%)	14 (2.0%)	1 (0.9%)
3	264 (7.4%)	30 (7.9%)	4 (0.5%)	52 (14%)	35 (11%)	8 (8.0%)	64 (7.6%)	55 (7.8%)	16 (14%)
4	611 (17%)	82 (21%)	16 (2.1%)	49 (14%)	82 (25%)	28 (28%)	197 (23%)	134 (19%)	23 (21%)
5	925 (26%)	122 (32%)	91 (12%)	93 (26%)	103 (32%)	41 (41%)	228 (27%)	220 (31%)	27 (24%)
6	982 (27%)	99 (26%)	252 (34%)	89 (25%)	69 (21%)	18 (18%)	240 (28%)	191 (27%)	24 (21%)
7	692 (19%)	32 (8.4%)	384 (51%)	39 (11%)	25 (7.8%)	0 (0%)	105 (12%)	90 (13%)	17 (15%)
1 n (%)
2 NA

3.2 Item-level correct response rate

# Proportion correct for each knowledge item, stratified by country
df |> 
  select(country, q1_sugar:q7_risk) |> 
  # Convert to factors so gtsummary shows proportions
  mutate(across(q1_sugar:q7_risk, ~ factor(.x, levels = c(1, 0),
                                            labels = c("Correct", "Incorrect")))) |> 
  tbl_summary(
    by = country,
    label = list(
      q1_sugar       ~ "Sugar intake",
      q2_first_visit ~ "First visit age",
      q3_fluoride    ~ "Fluoride",
      q4_sealants    ~ "Sealants",
      q5_ecc         ~ "ECC",
      q6_brushing    ~ "Brushing",
      q7_risk        ~ "Risk factors"
    ),
    value = list(q1_sugar ~ "Correct", q2_first_visit ~ "Correct",
                 q3_fluoride ~ "Correct", q4_sealants ~ "Correct",
                 q5_ecc ~ "Correct", q6_brushing ~ "Correct",
                 q7_risk ~ "Correct"),
    missing = "no"
  ) |> 
  add_overall() |> 
  add_p(test = list(all_categorical() ~ "chisq.test")) |> 
  bold_labels()

Characteristic	Overall N = 3,5771	Argentina N = 3821	Chile N = 7481	India N = 3601	Italy N = 3221	Latvia N = 1001	Serbia N = 8451	Spain N = 7081	Switzerland N = 1121	p-value2
Sugar intake	2,428 (68%)	219 (58%)	593 (79%)	212 (59%)	174 (54%)	77 (77%)	551 (65%)	516 (74%)	86 (77%)	<0.001
First visit age	2,788 (78%)	344 (91%)	731 (98%)	262 (73%)	229 (71%)	73 (73%)	631 (75%)	466 (66%)	52 (47%)	<0.001
Fluoride	2,694 (76%)	199 (53%)	642 (86%)	218 (62%)	209 (65%)	77 (77%)	773 (91%)	504 (73%)	72 (64%)	<0.001
Sealants	1,952 (55%)	204 (54%)	688 (92%)	262 (73%)	139 (44%)	45 (45%)	301 (36%)	257 (37%)	56 (50%)	<0.001
ECC	3,271 (92%)	337 (90%)	699 (93%)	302 (85%)	308 (97%)	89 (89%)	786 (93%)	656 (94%)	94 (85%)	<0.001
Brushing	3,341 (94%)	344 (91%)	729 (97%)	263 (74%)	303 (96%)	95 (95%)	818 (97%)	679 (97%)	110 (99%)	<0.001
Risk factors	2,281 (64%)	232 (61%)	651 (87%)	156 (44%)	191 (60%)	2 (2.0%)	448 (53%)	528 (75%)	73 (65%)	<0.001
1 n (%)
2 Pearson’s Chi-squared test

3.3 Knowledge by specialty

# Knowledge score stratified by specialty
df |> 
  select(country, age_group, gender_label, specialty_label) |> 
  tbl_summary(
    by = country,
    label = list(
      age_group       ~ "Age group",
      gender_label    ~ "Gender",
      specialty_label ~ "Dental specialty"
    ),
    missing = "ifany",
    missing_text = "Missing"
  ) |> 
  add_overall() |> 
  add_p(test = list(all_categorical() ~ "chisq.test.no.correct"),
        test.args = all_tests("chisq.test.no.correct") ~ 
          list(simulate.p.value = TRUE, B = 2000)) |> 
  modify_header(label ~ "**Characteristic**") |> 
  bold_labels()

Characteristic	Overall N = 3,5771	Argentina N = 3821	Chile N = 7481	India N = 3601	Italy N = 3221	Latvia N = 1001	Serbia N = 8451	Spain N = 7081	Switzerland N = 1121	p-value2
Age group										<0.001
25-34	1,098 (31%)	86 (23%)	280 (37%)	318 (88%)	51 (16%)	27 (27%)	183 (22%)	130 (18%)	23 (21%)
35-44	981 (27%)	82 (21%)	279 (37%)	28 (7.8%)	47 (15%)	33 (33%)	268 (32%)	208 (29%)	36 (32%)
45-54	749 (21%)	122 (32%)	106 (14%)	13 (3.6%)	56 (17%)	23 (23%)	232 (27%)	178 (25%)	19 (17%)
55-64	526 (15%)	73 (19%)	58 (7.8%)	1 (0.3%)	105 (33%)	16 (16%)	128 (15%)	118 (17%)	27 (24%)
65-74	201 (5.6%)	15 (3.9%)	24 (3.2%)	0 (0%)	58 (18%)	1 (1.0%)	31 (3.7%)	67 (9.5%)	5 (4.5%)
75+	22 (0.6%)	4 (1.0%)	1 (0.1%)	0 (0%)	5 (1.6%)	0 (0%)	3 (0.4%)	7 (1.0%)	2 (1.8%)
Gender										<0.001
Male	1,118 (31%)	77 (20%)	237 (32%)	108 (30%)	212 (66%)	5 (5.0%)	196 (23%)	236 (33%)	47 (43%)
Female	2,455 (69%)	305 (80%)	511 (68%)	252 (70%)	110 (34%)	95 (95%)	647 (77%)	472 (67%)	63 (57%)
Missing	4	0	0	0	0	0	2	0	2
Dental specialty										<0.001
General Dentistry	1,825 (51%)	172 (45%)	438 (59%)	147 (41%)	128 (40%)	82 (82%)	480 (57%)	350 (49%)	28 (25%)
Pediatric Dentistry	518 (14%)	98 (26%)	140 (19%)	19 (5.3%)	13 (4.0%)	11 (11%)	152 (18%)	64 (9.0%)	21 (19%)
Orthodontics	240 (6.7%)	32 (8.4%)	29 (3.9%)	9 (2.5%)	36 (11%)	1 (1.0%)	44 (5.2%)	75 (11%)	14 (13%)
Oral Surgery	262 (7.3%)	21 (5.5%)	25 (3.3%)	22 (6.1%)	39 (12%)	0 (0%)	55 (6.5%)	96 (14%)	4 (3.6%)
Restorative/Endo	414 (12%)	42 (11%)	34 (4.5%)	146 (41%)	45 (14%)	2 (2.0%)	40 (4.7%)	79 (11%)	26 (23%)
Prosthodontics	246 (6.9%)	12 (3.1%)	78 (10%)	9 (2.5%)	41 (13%)	2 (2.0%)	54 (6.4%)	38 (5.4%)	12 (11%)
Periodontics	72 (2.0%)	5 (1.3%)	4 (0.5%)	8 (2.2%)	20 (6.2%)	2 (2.0%)	20 (2.4%)	6 (0.8%)	7 (6.3%)
1 n (%)
2 Pearson’s Chi-squared test with simulated p-value (based on 2000 replicates)

3.4 Kruskal-Wallis: knowledge by country

# Non-parametric comparison given ordinal nature of the score
kruskal.test(score_knowledge ~ country, data = df)

    Kruskal-Wallis rank sum test

data:  score_knowledge by country
Kruskal-Wallis chi-squared = 737.42, df = 7, p-value < 2.2e-16

3.5 Kruskal-Wallis: knowledge by specialty

# Compare knowledge scores accross specialties
kruskal.test(score_knowledge ~ specialty_label, data = df)

    Kruskal-Wallis rank sum test

data:  score_knowledge by specialty_label
Kruskal-Wallis chi-squared = 150.6, df = 6, p-value < 2.2e-16

3.6 Knowledge by gender

# Knowledge score comparison by gender
df |> 
  filter(!is.na(gender_label)) |> 
  select(gender_label, score_knowledge) |> 
  tbl_summary(
    by = gender_label,
    label = list(score_knowledge ~ "Knowledge score (0-7)"),
    statistic = list(all_continuous() ~ "{mean} ({sd}) | {median} [{p25}, {p75}]"),
    missing = "no"
  ) |> 
  bold_labels()

Characteristic	Male N = 1,1181	Female N = 2,4551
Knowledge score (0-7)
0	5 (0.4%)	2 (<0.1%)
1	11 (1.0%)	17 (0.7%)
2	25 (2.2%)	43 (1.8%)
3	95 (8.5%)	169 (6.9%)
4	197 (18%)	414 (17%)
5	296 (26%)	628 (26%)
6	286 (26%)	695 (28%)
7	203 (18%)	487 (20%)
1 n (%)

4 Attitudes

Likert scale: 1 = Strongly agree, 2 = Agree, 3 = Disagree, 4 = Strongly disagree.

4.1 Attitude items by country

# Attitude items as ordered factors, stratified by country
df |> 
  select(country, q8_smoking, q9_paed_prev, q10_equipped) |> 
  mutate(across(q8_smoking:q10_equipped,
                ~ factor(.x, levels = 1:4,
                         labels = c("Strongly agree", "Agree",
                                    "Disagree", "Strongly disagree")))) |> 
  tbl_summary(
    by = country,
    label = list(
      q8_smoking   ~ "Preventive activities: smoking cessation",
      q9_paed_prev ~ "Preventive activities: paediatric dentistry",
      q10_equipped ~ "Well equipped for preventive care"
    ),
    missing = "no"
  ) |> 
  add_overall() |> 
  add_p(test = list(all_categorical() ~ "chisq.test")) |> 
  bold_labels()

Characteristic	Overall N = 3,5771	Argentina N = 3821	Chile N = 7481	India N = 3601	Italy N = 3221	Latvia N = 1001	Serbia N = 8451	Spain N = 7081	Switzerland N = 1121	p-value2
Preventive activities: smoking cessation										<0.001
Strongly agree	1,956 (57%)	320 (84%)	579 (77%)	240 (67%)	170 (53%)	15 (15%)	24 (2.8%)	608 (86%)	0 (NA%)
Agree	496 (14%)	0 (0%)	134 (18%)	88 (25%)	116 (36%)	29 (29%)	129 (15%)	0 (0%)	0 (NA%)
Disagree	512 (15%)	54 (14%)	31 (4.1%)	28 (7.9%)	33 (10%)	30 (30%)	248 (29%)	88 (12%)	0 (NA%)
Strongly disagree	495 (14%)	8 (2.1%)	4 (0.5%)	0 (0%)	3 (0.9%)	26 (26%)	444 (53%)	10 (1.4%)	0 (NA%)
Preventive activities: paediatric dentistry										<0.001
Strongly agree	2,342 (66%)	374 (98%)	704 (94%)	230 (65%)	250 (78%)	13 (13%)	3 (0.4%)	695 (99%)	73 (65%)
Agree	264 (7.4%)	0 (0%)	38 (5.1%)	101 (29%)	61 (19%)	2 (2.0%)	32 (3.8%)	0 (0%)	30 (27%)
Disagree	192 (5.4%)	5 (1.3%)	3 (0.4%)	23 (6.5%)	9 (2.8%)	17 (17%)	118 (14%)	9 (1.3%)	8 (7.1%)
Strongly disagree	768 (22%)	3 (0.8%)	3 (0.4%)	0 (0%)	0 (0%)	68 (68%)	692 (82%)	1 (0.1%)	1 (0.9%)
Well equipped for preventive care										<0.001
Strongly agree	1,478 (41%)	260 (68%)	311 (42%)	181 (51%)	71 (22%)	10 (10%)	108 (13%)	505 (72%)	32 (29%)
Agree	785 (22%)	0 (0%)	260 (35%)	96 (27%)	90 (28%)	11 (11%)	272 (32%)	0 (0%)	56 (50%)
Disagree	935 (26%)	104 (27%)	163 (22%)	78 (22%)	131 (41%)	29 (29%)	234 (28%)	173 (25%)	23 (21%)
Strongly disagree	368 (10%)	18 (4.7%)	14 (1.9%)	0 (0%)	27 (8.5%)	50 (50%)	231 (27%)	27 (3.8%)	1 (0.9%)
1 n (%)
2 Pearson’s Chi-squared test

5 Practice

Likert scale: 1 = Always, 2 = Often, 3 = Sometimes, 4 = Never.

5.1 Practice items by country

# Practice items as ordered factors, stratified by country
df |> 
  select(country, q11_sealants:q14_diet) |> 
  mutate(across(q11_sealants:q14_diet,
                ~ factor(.x, levels = 1:4,
                         labels = c("Always", "Often",
                                    "Sometimes", "Never")))) |> 
  tbl_summary(
    by = country,
    label = list(
      q11_sealants ~ "Apply dental sealants",
      q12_varnish  ~ "Apply fluoride varnish",
      q13_ohi      ~ "Give oral hygiene instructions",
      q14_diet     ~ "Provide dietary advice"
    ),
    missing = "no"
  ) |> 
  add_overall() |> 
  add_p(test = list(all_categorical() ~ "chisq.test")) |> 
  bold_labels()

Characteristic	Overall N = 3,5771	Argentina N = 3821	Chile N = 7481	India N = 3601	Italy N = 3221	Latvia N = 1001	Serbia N = 8451	Spain N = 7081	Switzerland N = 1121	p-value2
Apply dental sealants										<0.001
Always	1,602 (45%)	133 (35%)	343 (46%)	148 (42%)	154 (48%)	23 (23%)	514 (61%)	257 (37%)	30 (27%)
Often	1,264 (36%)	176 (46%)	281 (38%)	154 (44%)	121 (38%)	44 (44%)	242 (29%)	210 (30%)	36 (33%)
Sometimes	493 (14%)	56 (15%)	81 (11%)	45 (13%)	32 (10%)	20 (20%)	68 (8.0%)	166 (24%)	25 (23%)
Never	199 (5.6%)	17 (4.5%)	42 (5.6%)	7 (2.0%)	13 (4.1%)	13 (13%)	21 (2.5%)	67 (9.6%)	19 (17%)
Apply fluoride varnish										<0.001
Always	1,384 (39%)	156 (41%)	587 (79%)	124 (35%)	71 (22%)	25 (25%)	161 (19%)	219 (31%)	41 (37%)
Often	1,110 (31%)	140 (37%)	139 (19%)	148 (42%)	108 (34%)	45 (45%)	240 (28%)	254 (36%)	36 (32%)
Sometimes	729 (20%)	63 (16%)	14 (1.9%)	74 (21%)	86 (27%)	27 (27%)	270 (32%)	178 (25%)	17 (15%)
Never	335 (9.4%)	23 (6.0%)	7 (0.9%)	8 (2.3%)	55 (17%)	3 (3.0%)	174 (21%)	48 (6.9%)	17 (15%)
Give oral hygiene instructions										<0.001
Always	2,959 (83%)	353 (92%)	608 (82%)	261 (73%)	269 (85%)	75 (75%)	695 (82%)	625 (89%)	73 (65%)
Often	550 (15%)	28 (7.3%)	124 (17%)	69 (19%)	44 (14%)	25 (25%)	145 (17%)	78 (11%)	37 (33%)
Sometimes	46 (1.3%)	1 (0.3%)	14 (1.9%)	21 (5.9%)	4 (1.3%)	0 (0%)	4 (0.5%)	1 (0.1%)	1 (0.9%)
Never	8 (0.2%)	0 (0%)	0 (0%)	5 (1.4%)	1 (0.3%)	0 (0%)	1 (0.1%)	0 (0%)	1 (0.9%)
Provide dietary advice										<0.001
Always	1,689 (47%)	253 (67%)	254 (34%)	215 (61%)	156 (49%)	32 (32%)	311 (37%)	409 (58%)	59 (54%)
Often	1,289 (36%)	95 (25%)	303 (41%)	109 (31%)	124 (39%)	54 (54%)	338 (40%)	223 (32%)	43 (39%)
Sometimes	513 (14%)	28 (7.4%)	162 (22%)	25 (7.1%)	34 (11%)	14 (14%)	175 (21%)	68 (9.6%)	7 (6.4%)
Never	68 (1.9%)	4 (1.1%)	27 (3.6%)	3 (0.9%)	6 (1.9%)	0 (0%)	21 (2.5%)	6 (0.8%)	1 (0.9%)
1 n (%)
2 Pearson’s Chi-squared test

5.2 Practice by age group

# Practice items stratified by age group
df |> 
  select(age_group, q11_sealants:q14_diet) |> 
  mutate(across(q11_sealants:q14_diet,
                ~ factor(.x, levels = 1:4,
                         labels = c("Always", "Often",
                                    "Sometimes", "Never")))) |> 
  tbl_summary(
    by = age_group,
    label = list(
      q11_sealants ~ "Apply dental sealants",
      q12_varnish  ~ "Apply fluoride varnish",
      q13_ohi      ~ "Give oral hygiene instructions",
      q14_diet     ~ "Provide dietary advice"
    ),
    missing = "no"
  ) |> 
  add_p(test = list(all_categorical() ~ "chisq.test")) |> 
  bold_labels()

Characteristic	25-34 N = 1,0981	35-44 N = 9811	45-54 N = 7491	55-64 N = 5261	65-74 N = 2011	75+ N = 221	p-value2
Apply dental sealants							<0.001
Always	466 (43%)	425 (43%)	359 (48%)	249 (48%)	94 (47%)	9 (43%)
Often	454 (42%)	326 (33%)	242 (33%)	169 (32%)	65 (32%)	8 (38%)
Sometimes	135 (12%)	160 (16%)	98 (13%)	68 (13%)	30 (15%)	2 (9.5%)
Never	36 (3.3%)	69 (7.0%)	44 (5.9%)	36 (6.9%)	12 (6.0%)	2 (9.5%)
Apply fluoride varnish							<0.001
Always	483 (44%)	402 (41%)	266 (36%)	162 (31%)	65 (33%)	6 (27%)
Often	330 (30%)	309 (32%)	221 (30%)	168 (32%)	73 (37%)	9 (41%)
Sometimes	204 (19%)	180 (18%)	166 (22%)	125 (24%)	48 (24%)	6 (27%)
Never	73 (6.7%)	89 (9.1%)	91 (12%)	67 (13%)	14 (7.0%)	1 (4.5%)
Give oral hygiene instructions							<0.001
Always	853 (78%)	793 (81%)	641 (86%)	472 (90%)	181 (91%)	19 (86%)
Often	209 (19%)	176 (18%)	98 (13%)	47 (9.0%)	18 (9.0%)	2 (9.1%)
Sometimes	25 (2.3%)	11 (1.1%)	6 (0.8%)	4 (0.8%)	0 (0%)	0 (0%)
Never	4 (0.4%)	0 (0%)	1 (0.1%)	2 (0.4%)	0 (0%)	1 (4.5%)
Provide dietary advice							<0.001
Always	428 (39%)	429 (44%)	414 (56%)	295 (56%)	115 (58%)	8 (38%)
Often	425 (39%)	361 (37%)	252 (34%)	172 (33%)	67 (34%)	12 (57%)
Sometimes	205 (19%)	164 (17%)	71 (9.5%)	55 (10%)	17 (8.5%)	1 (4.8%)
Never	30 (2.8%)	25 (2.6%)	8 (1.1%)	4 (0.8%)	1 (0.5%)	0 (0%)
1 n (%)
2 Pearson’s Chi-squared test

6 Barriers

Likert scale: 1 = Strongly disagree, 2 = Disagree, 3 = Agree, 4 = Strongly agree.

6.1 Barrier items by country

# Barrier items as ordered factors, stratified by country
# Includes overall column and chi-squared p-values
df |> 
  select(country, q15_time:q20_training) |> 
  mutate(across(q15_time:q20_training,
                ~ factor(.x, levels = 1:4,
                         labels = c("Strongly disagree", "Disagree",
                                    "Agree", "Strongly agree")))) |> 
  tbl_summary(
    by = country,
    label = list(
      q15_time         ~ "Lack of time",
      q16_remuneration ~ "Lack of remuneration",
      q17_motivation   ~ "Lack of motivation",
      q18_compliance   ~ "Poor patient compliance",
      q19_knowledge_b  ~ "Lack of knowledge",
      q20_training     ~ "Lack of training"
    ),
    missing = "no"
  ) |> 
  add_overall() |> 
  add_p(test = list(all_categorical() ~ "chisq.test")) |> 
  bold_labels()

Characteristic	Overall N = 3,5771	Argentina N = 3821	Chile N = 7481	India N = 3601	Italy N = 3221	Latvia N = 1001	Serbia N = 8451	Spain N = 7081	Switzerland N = 1121	p-value2
Lack of time										<0.001
Strongly disagree	1,443 (40%)	164 (43%)	402 (54%)	134 (38%)	29 (9.1%)	23 (23%)	267 (32%)	409 (58%)	15 (13%)
Disagree	775 (22%)	0 (0%)	239 (32%)	104 (29%)	75 (24%)	17 (17%)	283 (33%)	0 (0%)	57 (51%)
Agree	834 (23%)	102 (27%)	92 (12%)	118 (33%)	109 (34%)	37 (37%)	201 (24%)	151 (21%)	24 (21%)
Strongly agree	515 (14%)	116 (30%)	14 (1.9%)	0 (0%)	106 (33%)	23 (23%)	94 (11%)	146 (21%)	16 (14%)
Lack of remuneration										<0.001
Strongly disagree	1,260 (35%)	207 (54%)	114 (15%)	112 (32%)	40 (13%)	16 (16%)	353 (42%)	407 (58%)	11 (9.8%)
Disagree	657 (18%)	0 (0%)	192 (26%)	104 (30%)	75 (23%)	26 (26%)	233 (28%)	0 (0%)	27 (24%)
Agree	1,028 (29%)	82 (22%)	310 (42%)	130 (38%)	109 (34%)	35 (35%)	160 (19%)	152 (22%)	50 (45%)
Strongly agree	610 (17%)	92 (24%)	130 (17%)	0 (0%)	96 (30%)	23 (23%)	99 (12%)	146 (21%)	24 (21%)
Lack of motivation										<0.001
Strongly disagree	1,473 (41%)	197 (52%)	444 (60%)	116 (33%)	20 (6.3%)	24 (24%)	305 (36%)	364 (52%)	3 (2.7%)
Disagree	672 (19%)	0 (0%)	209 (28%)	122 (34%)	48 (15%)	26 (26%)	232 (27%)	0 (0%)	35 (32%)
Agree	870 (24%)	100 (26%)	78 (10%)	117 (33%)	100 (31%)	30 (30%)	209 (25%)	191 (27%)	45 (41%)
Strongly agree	544 (15%)	84 (22%)	15 (2.0%)	0 (0%)	151 (47%)	20 (20%)	99 (12%)	148 (21%)	27 (25%)
Poor patient compliance										<0.001
Strongly disagree	1,087 (30%)	195 (51%)	214 (29%)	117 (33%)	39 (12%)	8 (8.0%)	63 (7.5%)	438 (62%)	13 (12%)
Disagree	693 (19%)	0 (0%)	178 (24%)	137 (39%)	106 (33%)	12 (12%)	211 (25%)	0 (0%)	49 (44%)
Agree	1,102 (31%)	118 (31%)	149 (20%)	101 (28%)	125 (39%)	32 (32%)	348 (41%)	194 (27%)	35 (31%)
Strongly agree	685 (19%)	68 (18%)	207 (28%)	0 (0%)	50 (16%)	48 (48%)	223 (26%)	74 (10%)	15 (13%)
Lack of knowledge										<0.001
Strongly disagree	1,069 (30%)	197 (52%)	170 (23%)	115 (32%)	35 (11%)	21 (21%)	273 (32%)	252 (36%)	6 (5.4%)
Disagree	647 (18%)	0 (0%)	134 (18%)	102 (29%)	80 (25%)	29 (29%)	261 (31%)	0 (0%)	41 (37%)
Agree	1,039 (29%)	97 (26%)	172 (23%)	138 (39%)	117 (37%)	30 (30%)	211 (25%)	229 (32%)	45 (40%)
Strongly agree	810 (23%)	85 (22%)	272 (36%)	0 (0%)	88 (28%)	20 (20%)	100 (12%)	225 (32%)	20 (18%)
Lack of training										<0.001
Strongly disagree	1,165 (33%)	215 (56%)	188 (25%)	114 (32%)	24 (7.5%)	23 (23%)	307 (36%)	291 (41%)	3 (2.7%)
Disagree	719 (20%)	0 (0%)	252 (34%)	96 (27%)	55 (17%)	35 (35%)	253 (30%)	0 (0%)	28 (25%)
Agree	957 (27%)	79 (21%)	155 (21%)	145 (41%)	110 (34%)	23 (23%)	192 (23%)	204 (29%)	49 (44%)
Strongly agree	723 (20%)	87 (23%)	153 (20%)	0 (0%)	131 (41%)	19 (19%)	93 (11%)	209 (30%)	31 (28%)
1 n (%)
2 Pearson’s Chi-squared test

6.2 Barrier items by specialty

# Barriers stratified by speciaty
df |> 
  select(specialty_label, q15_time:q20_training) |> 
  mutate(across(q15_time:q20_training,
                ~ factor(.x, levels = 1:4,
                         labels = c("Strongly disagree", "Disagree",
                                    "Agree", "Strongly agree")))) |> 
  tbl_summary(
    by = specialty_label,
    label = list(
      q15_time         ~ "Lack of time",
      q16_remuneration ~ "Lack of remuneration",
      q17_motivation   ~ "Lack of motivation",
      q18_compliance   ~ "Poor patient compliance",
      q19_knowledge_b  ~ "Lack of knowledge",
      q20_training     ~ "Lack of training"
    ),
    missing = "no"
  ) |> 
  add_p(test = list(all_categorical() ~ "chisq.test")) |> 
  bold_labels()

Characteristic	General Dentistry N = 1,8251	Pediatric Dentistry N = 5181	Orthodontics N = 2401	Oral Surgery N = 2621	Restorative/Endo N = 4141	Prosthodontics N = 2461	Periodontics N = 721	p-value2
Lack of time								<0.001
Strongly disagree	750 (41%)	218 (42%)	100 (42%)	117 (45%)	149 (36%)	90 (37%)	19 (27%)
Disagree	407 (22%)	93 (18%)	43 (18%)	36 (14%)	104 (25%)	67 (27%)	25 (35%)
Agree	422 (23%)	117 (23%)	53 (22%)	67 (26%)	108 (26%)	51 (21%)	16 (23%)
Strongly agree	242 (13%)	89 (17%)	44 (18%)	41 (16%)	52 (13%)	36 (15%)	11 (15%)
Lack of remuneration								0.2
Strongly disagree	640 (35%)	194 (38%)	82 (34%)	109 (42%)	151 (37%)	67 (27%)	17 (24%)
Disagree	339 (19%)	95 (18%)	37 (15%)	40 (15%)	82 (20%)	47 (19%)	17 (24%)
Agree	528 (29%)	142 (28%)	73 (30%)	69 (26%)	113 (28%)	79 (32%)	24 (34%)
Strongly agree	310 (17%)	83 (16%)	48 (20%)	43 (16%)	62 (15%)	51 (21%)	13 (18%)
Lack of motivation								<0.001
Strongly disagree	791 (44%)	246 (48%)	83 (35%)	98 (38%)	149 (36%)	83 (34%)	23 (32%)
Disagree	363 (20%)	80 (16%)	33 (14%)	37 (14%)	80 (19%)	67 (28%)	12 (17%)
Agree	419 (23%)	115 (22%)	67 (28%)	65 (25%)	128 (31%)	55 (23%)	21 (30%)
Strongly agree	244 (13%)	73 (14%)	56 (23%)	61 (23%)	57 (14%)	38 (16%)	15 (21%)
Poor patient compliance								<0.001
Strongly disagree	554 (30%)	133 (26%)	81 (34%)	107 (41%)	138 (33%)	61 (25%)	13 (19%)
Disagree	323 (18%)	104 (20%)	42 (18%)	43 (16%)	99 (24%)	55 (23%)	27 (39%)
Agree	590 (32%)	155 (30%)	70 (29%)	74 (28%)	124 (30%)	73 (30%)	16 (23%)
Strongly agree	354 (19%)	125 (24%)	47 (20%)	37 (14%)	53 (13%)	55 (23%)	14 (20%)
Lack of knowledge								<0.001
Strongly disagree	520 (29%)	179 (35%)	76 (32%)	75 (29%)	118 (29%)	84 (34%)	17 (24%)
Disagree	349 (19%)	74 (14%)	25 (11%)	42 (16%)	90 (22%)	52 (21%)	15 (21%)
Agree	517 (28%)	137 (26%)	79 (33%)	80 (31%)	140 (34%)	63 (26%)	23 (32%)
Strongly agree	434 (24%)	127 (25%)	58 (24%)	64 (25%)	66 (16%)	45 (18%)	16 (23%)
Lack of training								<0.001
Strongly disagree	591 (32%)	199 (38%)	69 (29%)	89 (34%)	132 (32%)	70 (29%)	15 (21%)
Disagree	385 (21%)	102 (20%)	32 (13%)	42 (16%)	76 (18%)	67 (27%)	15 (21%)
Agree	475 (26%)	119 (23%)	76 (32%)	64 (25%)	132 (32%)	69 (28%)	22 (31%)
Strongly agree	371 (20%)	97 (19%)	62 (26%)	64 (25%)	72 (17%)	38 (16%)	19 (27%)
1 n (%)
2 Pearson’s Chi-squared test

7 Visualizations

7.1 Knowledge score by country

# Boxplot ordered by median knowledge score
df |> 
  ggplot(aes(x = fct_reorder(country, score_knowledge, .fun = median),
             y = score_knowledge, fill = country)) +
  geom_boxplot(alpha = 0.8, show.legend = FALSE) +
  geom_jitter(alpha = 0.05, width = 0.2) +
  scale_fill_viridis_d() +
  labs(x = "Country", y = "Knowledge Score (0-7)") +
  coord_flip() +
  theme_minimal(base_size = 13) +
  theme(legend.position = "none")

ggsave(here("output", "fig1_knowledge_by_country.png"),
       width = 8, height = 5, dpi = 300)

Figure 1: Knowledge score distribution by country

7.2 Knowledge item heatmap

# Heatmap of item-level performace across countries
df |> 
  select(country, q1_sugar:q7_risk) |> 
  pivot_longer(cols = q1_sugar:q7_risk,
               names_to = "item",
               values_to = "correct") |> 
  mutate(
    item = case_when(
      item == "q1_sugar"       ~ "Sugar intake",
      item == "q2_first_visit" ~ "First visit age",
      item == "q3_fluoride"    ~ "Fluoride",
      item == "q4_sealants"    ~ "Sealants",
      item == "q5_ecc"         ~ "ECC",
      item == "q6_brushing"    ~ "Brushing",
      item == "q7_risk"        ~ "Risk factors"
    )
  ) |> 
  group_by(country, item) |> 
  summarise(pct_correct = mean(correct, na.rm = TRUE) * 100,
            .groups = "drop") |> 
  ggplot(aes(x = item, y = country, fill = pct_correct)) +
  geom_tile(color = "white", linewidth = 0.5) +
  geom_text(aes(label = sprintf("%.0f%%", pct_correct)), size = 3) +
  scale_fill_viridis_c(name = "% Correct", limits = c(0, 100)) +
  labs(x = NULL, y = NULL) +
  theme_minimal(base_size = 12) +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

ggsave(here("output", "fig2_knowledge_heatmap.png"),
       width = 9, height = 5, dpi = 300)

Figure 2: Correct response rate by knowledge item and country

7.3 Knowledge by specialty

# Violin + boxplot combination for specialty comparisons
df |> 
  ggplot(aes(x = fct_reorder(specialty_label, score_knowledge, .fun = median),
             y = score_knowledge, fill = specialty_label)) +
  geom_violin(alpha = 0.5, show.legend = FALSE) +
  geom_boxplot(width = 0.15, alpha = 0.8, show.legend = FALSE) +
  scale_fill_viridis_d() +
  labs(x = "Specialty", y = "Knowledge Score (0-7)") +
  coord_flip() +
  theme_minimal(base_size = 13) + 
  ggtitle("Knowledge score distribution by dental specialty")

ggsave(here("output", "fig6_knowledge_by_specialty.png"),
       width = 9, height = 5, dpi = 300)

Figure 3: Knowledge score distribution by dental specialty

df |> 
  ggplot(aes(x = fct_reorder(specialty_label, score_knowledge, .fun = median),
             y = score_knowledge, fill = specialty_label)) +
  geom_violin(alpha = 0.5, show.legend = FALSE) +
  geom_boxplot(width = 0.15, alpha = 0.8, show.legend = FALSE) +
  scale_fill_viridis_d() +
  labs(x = "Specialty", y = "Knowledge Score (0-7)") +
  coord_flip() +
  theme_minimal(base_size = 13) + 
  facet_grid( . ~ country) + 
  ggtitle("Knowledge score distribution by dental specialty and country")

ggsave(here("output", "fig6_1_knowledge_by_specialty_by_country.png"),
       width = 9, height = 5, dpi = 300)

7.4 Barriers: stacked bar chart

# Stacked bar chart for barrier Likert responses
df |> 
  select(q15_time:q20_training) |> 
  pivot_longer(cols = everything(),
               names_to = "barrier",
               values_to = "response") |> 
  filter(!is.na(response)) |> 
  mutate(
    response_label = factor(response,
                            levels = 1:4,
                            labels = c("Strongly disagree", "Disagree",
                                       "Agree", "Strongly agree")),
    barrier = case_when(
      barrier == "q15_time"         ~ "Lack of time",
      barrier == "q16_remuneration" ~ "Lack of remuneration",
      barrier == "q17_motivation"   ~ "Lack of motivation",
      barrier == "q18_compliance"   ~ "Poor compliance",
      barrier == "q19_knowledge_b"  ~ "Lack of knowledge",
      barrier == "q20_training"     ~ "Lack of training"
    )
  ) |> 
  count(barrier, response_label) |> 
  group_by(barrier) |> 
  mutate(pct = n / sum(n) * 100) |> 
  ungroup() |> 
  ggplot(aes(x = fct_rev(barrier), y = pct, fill = response_label)) +
  geom_col(position = "stack", width = 0.7) +
  scale_fill_viridis_d(name = "Response", option = "D") +
  labs(x = NULL, y = "Percentage (%)") +
  coord_flip() +
  theme_minimal(base_size = 13) + 
  theme(legend.position = "top") + 
  ggtitle("Perceived barriers to preventive dentistry (overall)")

ggsave(here("output", "fig3_barriers_stacked.png"),
       width = 9, height = 5, dpi = 300)

Figure 4: Perceived barriers to preventive dentistry (overall)

7.5 Barriers heatmap by country

# Heatmap showing barrier agreement rates across countries
df |> 
  select(country, q15_time:q20_training) |> 
  pivot_longer(cols = q15_time:q20_training,
               names_to = "barrier",
               values_to = "response") |> 
  filter(!is.na(response)) |> 
  mutate(
    agree = if_else(response >= 3, 1, 0),
    barrier = case_when(
      barrier == "q15_time"         ~ "Time",
      barrier == "q16_remuneration" ~ "Remuneration",
      barrier == "q17_motivation"   ~ "Motivation",
      barrier == "q18_compliance"   ~ "Patient compliance",
      barrier == "q19_knowledge_b"  ~ "Knowledge",
      barrier == "q20_training"     ~ "Training"
    )
  ) |> 
  group_by(country, barrier) |> 
  summarise(pct_agree = mean(agree) * 100, .groups = "drop") |> 
  ggplot(aes(x = barrier, y = country, fill = pct_agree)) +
  geom_tile(color = "white", linewidth = 0.5) +
  geom_text(aes(label = sprintf("%.0f%%", pct_agree)), size = 3) +
  scale_fill_viridis_c(name = "% Agree", option = "D") +
  labs(x = NULL, y = NULL) +
  theme_minimal(base_size = 12) +
  theme(axis.text.x = element_text(angle = 45, hjust = 1)) + 
  ggtitle("Perceived barriers by country (% Agree or Strongly Agree)")

ggsave(here("output", "fig5_barriers_heatmap.png"),
       width = 9, height = 5, dpi = 300)

Figure 5: Perceived barriers by country (% Agree or Strongly Agree)

7.6 Practice frequency by country

# Grouped bar chart for practice items across countries
df |> 
  select(country, q11_sealants:q14_diet) |> 
  pivot_longer(cols = q11_sealants:q14_diet,
               names_to = "practice",
               values_to = "response") |> 
  filter(!is.na(response)) |> 
  mutate(
    frequent = if_else(response <= 2, 1, 0),
    practice = case_when(
      practice == "q11_sealants" ~ "Sealants",
      practice == "q12_varnish"  ~ "Fluoride varnish",
      practice == "q13_ohi"      ~ "OHI",
      practice == "q14_diet"     ~ "Dietary advice"
    )
  ) |> 
  group_by(country, practice) |> 
  summarise(pct = mean(frequent) * 100, .groups = "drop") |> 
  ggplot(aes(x = country, y = pct, fill = practice)) +
  geom_col(position = position_dodge(width = 0.8), width = 0.7) +
  scale_fill_viridis_d(name = "Practice", option = "D") +
  labs(x = NULL, y = "% Always or Often") +
  theme_minimal(base_size = 12) +
  theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
  coord_flip() + 
  ggtitle("Preventive practice frequency by country (% Always or Often)")

ggsave(here("output", "fig4_practice_by_country.png"),
       width = 10, height = 5, dpi = 300)

Figure 6: Preventive practice frequency by country (% Always or Often)

7.7 Overall ranking of preventive practices

# Proportion reporting Always/Often for each practice item, overall
df |> 
  select(q11_sealants:q14_diet) |> 
  pivot_longer(cols = everything(),
               names_to = "practice",
               values_to = "response") |> 
  filter(!is.na(response)) |> 
  mutate(
    frequent = if_else(response <= 2, 1, 0),
    practice = case_when(
      practice == "q11_sealants" ~ "Sealants",
      practice == "q12_varnish"  ~ "Fluoride varnish",
      practice == "q13_ohi"      ~ "Oral hygiene instructions",
      practice == "q14_diet"     ~ "Dietary advice"
    )
  ) |> 
  group_by(practice) |> 
  summarise(pct = mean(frequent) * 100, .groups = "drop") |> 
  ggplot(aes(x = fct_reorder(practice, pct), y = pct, fill = pct)) +
  geom_col(width = 0.6) +
  geom_text(aes(label = sprintf("%.1f%%", pct)), hjust = -0.1, size = 4) +
  scale_fill_viridis_c(option = "D", guide = "none") +
  labs(x = NULL, y = "% Always or Often") +
  coord_flip(ylim = c(0, 100)) +
  theme_minimal(base_size = 13) + 
  ggtitle("Overall ranking of preventive practices (% Always or Often)")

ggsave(here("output", "fig7_practice_ranking_overall.png"),
       width = 8, height = 4, dpi = 300)

Figure 7: Overall ranking of preventive practices (% Always or Often)

7.8 Overall ranking of preventive practices by country

# Practice ranking faceted by country
df |> 
  select(country, q11_sealants:q14_diet) |> 
  pivot_longer(cols = q11_sealants:q14_diet,
               names_to = "practice",
               values_to = "response") |> 
  filter(!is.na(response)) |> 
  mutate(
    frequent = if_else(response <= 2, 1, 0),
    practice = case_when(
      practice == "q11_sealants" ~ "Sealants",
      practice == "q12_varnish"  ~ "Fluoride varnish",
      practice == "q13_ohi"      ~ "OHI",
      practice == "q14_diet"     ~ "Dietary advice"
    )
  ) |> 
  group_by(country, practice) |> 
  summarise(pct = mean(frequent) * 100, .groups = "drop") |> 
  ggplot(aes(x = fct_reorder(practice, pct), y = pct, fill = practice)) +
  geom_col(width = 0.7) +
  geom_text(aes(label = sprintf("%.0f%%", pct)), hjust = -0.1, size = 2.5) +
  scale_fill_viridis_d(option = "C", guide = "none") +
  labs(x = NULL, y = "% Always or Often") +
  coord_flip(ylim = c(0, 105)) +
  facet_wrap(~ country, ncol = 4) +
  theme_minimal(base_size = 11) +
  theme(strip.text = element_text(face = "bold")) + 
  ggtitle("Ranking of preventive practices by country (% Always or Often)")

ggsave(here("output", "fig8_practice_ranking_by_country.png"),
       width = 12, height = 6, dpi = 300)

Figure 8: Ranking of preventive practices by country (% Always or Often)

7.9 Distribution of preventive practice score

# Composite score: sum of all 4 practice items (range 4-16)
# Lower score = check"more frequent preventive practice?
df |> 
  mutate(
    practice_score = q11_sealants + q12_varnish + q13_ohi + q14_diet
  ) |> 
  filter(!is.na(practice_score)) |> 
  ggplot(aes(x = practice_score)) +
  geom_histogram(binwidth = 1, color = "white", linewidth = 0.3) +
  geom_vline(aes(xintercept = mean(practice_score, na.rm = TRUE)),
             linetype = "dashed", color = "red", linewidth = 0.8) +
  # scale_fill_viridis_c(option = "D", guide = "none") +
  scale_x_continuous(breaks = 0:16) +
  labs(
    title = "Distribution of composite preventive practice score", 
    x = "Preventive Practice Score (0 = best, 16 = worst)",
    y = "Frequency"
  ) +
  annotate("text", x = 14, y = Inf, vjust = 2, hjust = 1, size = 3.5,
           label = "Dashed line = mean") +
  theme_minimal(base_size = 13)

ggsave(here("output", "fig9_practice_score_histogram.png"),
       width = 8, height = 5, dpi = 300)

Figure 9: Distribution of composite preventive practice score

8 Summary table

# Recode practice and barrier items into binary for a sake of clarity
df |> 
  transmute(
    country,
    score_knowledge,
    # Practice: binary always/often
    sealants_frequent = if_else(q11_sealants <= 2, 1L, 0L),
    varnish_frequent  = if_else(q12_varnish <= 2, 1L, 0L),
    ohi_frequent      = if_else(q13_ohi <= 2, 1L, 0L),
    diet_frequent     = if_else(q14_diet <= 2, 1L, 0L),
    # Barriers: binary agree/strongly agree
    barrier_time         = if_else(q15_time >= 3, 1L, 0L),
    barrier_remuneration = if_else(q16_remuneration >= 3, 1L, 0L),
    barrier_motivation   = if_else(q17_motivation >= 3, 1L, 0L),
    barrier_compliance   = if_else(q18_compliance >= 3, 1L, 0L),
    barrier_knowledge    = if_else(q19_knowledge_b >= 3, 1L, 0L),
    barrier_training     = if_else(q20_training >= 3, 1L, 0L)
  ) |> 
  tbl_summary(
    by = country,
    label = list(
      score_knowledge      ~ "Knowledge score (0-7)",
      sealants_frequent    ~ "Sealants (Always/Often)",
      varnish_frequent     ~ "Fluoride varnish (Always/Often)",
      ohi_frequent         ~ "OHI (Always/Often)",
      diet_frequent        ~ "Dietary advice (Always/Often)",
      barrier_time         ~ "Barrier: lack of time",
      barrier_remuneration ~ "Barrier: lack of remuneration",
      barrier_motivation   ~ "Barrier: lack of motivation",
      barrier_compliance   ~ "Barrier: poor compliance",
      barrier_knowledge    ~ "Barrier: lack of knowledge",
      barrier_training     ~ "Barrier: lack of training"
    ),
    statistic = list(
      all_continuous()  ~ "{mean} ({sd})",
      all_categorical() ~ "{p}%"
    ),
    missing = "no"
  ) |> 
  add_overall() |> 
  bold_labels()

Characteristic	Overall N = 3,5771	Argentina N = 3821	Chile N = 7481	India N = 3601	Italy N = 3221	Latvia N = 1001	Serbia N = 8451	Spain N = 7081	Switzerland N = 1121
Knowledge score (0-7)
0	0.2%	0%	0%	0.8%	0%	0%	0%	0.4%	0.9%
1	0.8%	0.3%	0%	5.3%	0%	1.0%	0.4%	0.1%	2.7%
2	1.9%	4.2%	0.1%	4.4%	2.5%	4.0%	0.9%	2.0%	0.9%
3	7.4%	7.9%	0.5%	14%	11%	8.0%	7.6%	7.8%	14%
4	17%	21%	2.1%	14%	25%	28%	23%	19%	21%
5	26%	32%	12%	26%	32%	41%	27%	31%	24%
6	27%	26%	34%	25%	21%	18%	28%	27%	21%
7	19%	8.4%	51%	11%	7.8%	0%	12%	13%	15%
Sealants (Always/Often)	81%	81%	84%	85%	86%	67%	89%	67%	60%
Fluoride varnish (Always/Often)	70%	77%	97%	77%	56%	70%	47%	68%	69%
OHI (Always/Often)	98%	100%	98%	93%	98%	100%	99%	100%	98%
Dietary advice (Always/Often)	84%	92%	75%	92%	88%	86%	77%	90%	93%
Barrier: lack of time	38%	57%	14%	33%	67%	60%	35%	42%	36%
Barrier: lack of remuneration	46%	46%	59%	38%	64%	58%	31%	42%	66%
Barrier: lack of motivation	40%	48%	12%	33%	79%	50%	36%	48%	65%
Barrier: poor compliance	50%	49%	48%	28%	55%	80%	68%	38%	45%
Barrier: lack of knowledge	52%	48%	59%	39%	64%	50%	37%	64%	58%
Barrier: lack of training	47%	44%	41%	41%	75%	42%	34%	59%	72%
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