Frequency distribution of patients admitted for substance-related disorders, in Santo Domingo, during 2019-2022
Andrés Aybar
2025-05-13
Introduction: Substance-related problems affect some individuals who engage in psychoactive substance use. In recent decades, the global demand for treatment in rehabilitation programs has varied depending on the substance for which individuals seek treatment. In the Dominican Republic, there is a lack of studies indicating which substances have the greatest impact on the population, with an emphasis on the demand in treatment centers. Methodology: This is a descriptive study with a quantitative approach using a non-experimental cross-sectional design. Data were collected from records archived at a private rehabilitation center in Santo Domingo, covering all patients admitted for substance use between 2019 and 2022, with a total of 337 cases. The purpose of this study is to analyze the frequency distribution according to the substance that led each patient to seek treatment and to compare trends over the years. Results: Over the four years, admissions related to cannabis use ranged from 44.2% to 51.6%. When categorizing the data by age range, out of the total sample (N=337), 123 patients were in the 14 to 24 age group, of whom 80.5% were admitted for cannabis use. When isolating the group with dual diagnosis and classifying the patients by disorder type, 88.9% of these patients with a psychotic disorder were admitted for cannabis use. Conclusion: A consistently high demand for treatment related to cannabis use was observed over four consecutive years. Additionally, a significant number of young individuals with cannabis related problems also exhibited other mental health disorders.
1 - Load and process the data
**1.1 - Load the packages and the data
## load packages
library(readxl)
library(dplyr)
library(missForest)
## load data base
data <- read_excel("Data_2019_2022.xlsx")
## remove unnecessary variables
data <- data %>% select(-codigo)**1.2 - Check the database and the class of the variables
head(data)## # A tibble: 6 × 10
## fecha_ingreso edad sexo nacionalidad estado_civil nivel_educativo
## <dttm> <dbl> <chr> <chr> <chr> <chr>
## 1 2019-01-01 00:00:00 17 M ITALIANO SOLTERO PRIMARIA
## 2 2019-01-05 00:00:00 21 F DOMINICANO SOLTERO SECUNDARIA
## 3 2019-01-08 00:00:00 18 M AMERICANO SOLTERO SECUNDARIA
## 4 2019-01-16 00:00:00 44 F DOMINICANO CASADO UNIVERSITARIA
## 5 2019-01-28 00:00:00 18 M DOMINICANO SOLTERO SECUNDARIA
## 6 2019-02-05 00:00:00 14 M DOMINICANO SOLTERO SECUNDARIA
## # ℹ 4 more variables: condicion_laboral <chr>, convivencia <chr>,
## # sustancia <chr>, pat_dual <chr>dim(data)## [1] 337 10## check the class of each variable
sapply(data, class)## $fecha_ingreso
## [1] "POSIXct" "POSIXt"
##
## $edad
## [1] "numeric"
##
## $sexo
## [1] "character"
##
## $nacionalidad
## [1] "character"
##
## $estado_civil
## [1] "character"
##
## $nivel_educativo
## [1] "character"
##
## $condicion_laboral
## [1] "character"
##
## $convivencia
## [1] "character"
##
## $sustancia
## [1] "character"
##
## $pat_dual
## [1] "character"**1.3 - Change class for imputation
## count missing values for each variable
na_counts <- sapply(data, function(x) sum(is.na(x)))
na_counts## fecha_ingreso edad sexo nacionalidad
## 0 3 4 5
## estado_civil nivel_educativo condicion_laboral convivencia
## 14 28 52 34
## sustancia pat_dual
## 4 0## change the class of character variables to factor (for imputation)
data <- data %>%
mutate(
fecha_ingreso = as.factor(fecha_ingreso),
sexo = as.factor(sexo),
nacionalidad = as.factor(nacionalidad),
estado_civil = as.factor(estado_civil),
nivel_educativo = as.factor(nivel_educativo),
condicion_laboral = as.factor(condicion_laboral),
convivencia = as.factor(convivencia),
sustancia = as.factor(sustancia),
pat_dual = as.factor(pat_dual)
)
## temporarily remove date
data2 <- data %>% select(-fecha_ingreso)
## set the database as.data.frame
data2 <- as.data.frame(data2)2 - Imputation
## do the imputation
imp <- missForest(data2, verbose = TRUE, variablewise = TRUE)## missForest iteration 1 in progress...done!
## estimated error(s): 96.88216 0.2282282 0.2590361 0.2136223 0.4789644 0.3263158 0.7491749 0.5825826 0
## difference(s): 0.0003159123 0.02485163
## time: 6.53 seconds
##
## missForest iteration 2 in progress...done!
## estimated error(s): 88.08359 0.2192192 0.2439759 0.2043344 0.4466019 0.3192982 0.7161716 0.5555556 0
## difference(s): 3.797234e-05 0.008531157
## time: 5.7 seconds
##
## missForest iteration 3 in progress...done!
## estimated error(s): 88.50904 0.2042042 0.2319277 0.2074303 0.4401294 0.3052632 0.7128713 0.5585586 0
## difference(s): 4.711076e-06 0.004821958
## time: 5.78 seconds
##
## missForest iteration 4 in progress...done!
## estimated error(s): 89.87556 0.1891892 0.25 0.2043344 0.4724919 0.3368421 0.7392739 0.5255255 0
## difference(s): 3.521031e-05 0.006305638
## time: 5.83 seconds# check error estimation
imp$OOBerror## MSE PFC PFC PFC PFC PFC PFC
## 88.5090389 0.2042042 0.2319277 0.2074303 0.4401294 0.3052632 0.7128713
## PFC PFC
## 0.5585586 0.0000000## create new complete data set
dflimpio <- as.data.frame(imp$ximp)
View(dflimpio)
## add back the date column
dflimpio <- dflimpio %>%
mutate(fecha_ingreso = data$fecha_ingreso) %>%
select(fecha_ingreso, everything())
## make the age variable into discrete integers again
dflimpio <- dflimpio %>%
mutate(edad = as.integer(edad))
## make the date variable back to date format
dflimpio <- dflimpio %>%
mutate(fecha_ingreso = as.Date(as.character(fecha_ingreso), format = "%Y-%m-%d"))3 - Create new variables
## add new variable año (just the year the patient was admitted)
dflimpio <- dflimpio %>%
mutate(año = format(fecha_ingreso, "%Y")) %>%
relocate(año, .after = fecha_ingreso)
## add new variable rangos_edad (age groups)
dflimpio <- dflimpio %>%
mutate(
rangos_edad = cut(
edad,
breaks = seq(10, max(edad, na.rm = TRUE) + 5, by = 5),
right = FALSE,
labels = paste(
seq(10, max(edad, na.rm = TRUE), by = 5),
seq(14, max(edad, na.rm = TRUE) + 4, by = 5),
sep = "-"
)
)
) %>%
relocate(rangos_edad, .after = edad)
## add new variable sustancia2 for new groups
dflimpio <- dflimpio %>%
mutate(sustancia2 = case_when(
sustancia == "THC" ~ "CANNABIS",
sustancia %in% c("FENTANILO", "HEROINA", "OPIOIDE", "PERCOCET") ~ "OPIOIDES",
sustancia %in% c("ANABOLICO", "BENZODIACEPINA", "EXTASIS", "INHALANTES", "LUDOPATIA", "METANFETAMINA", "TABACO") ~ "OTRAS SUSTANCIA O ADICCIONES",
TRUE ~ sustancia))
## add new variable convivencia2
dflimpio <- dflimpio %>%
mutate(convivencia2 = case_when(
convivencia %in% c("MADRE", "MADRE Y PADRASTRO", "PADRE", "PADRES") ~ "ALGUN PADRE",
convivencia == "HIJOS" ~ "HIJOS",
convivencia %in% c("ABUELA", "COMPANERO", "HERMANO", "PARIENTE") ~ "OTRO",
convivencia == "PAREJA" ~ "PAREJA",
convivencia == "PAREJA E HIJOS" ~ "PAREJA E HIJOS",
convivencia == "SOLO" ~ "SOLO",
TRUE ~ convivencia))
## add new variable pat_dual2
dflimpio <- dflimpio %>%
mutate(pat_dual2 = case_when(
pat_dual == "DEPRESION" ~ "DEPRESION",
pat_dual == "LUDOPATIA" ~ "LUDOPATIA",
pat_dual == "NO" ~ "NO",
pat_dual == "ANTISOCIAL" ~ "ANTISOCIAL",
pat_dual == "BIPOLAR" ~ "BIPOLAR",
pat_dual == "LIMITE" ~ "LIMITE",
pat_dual %in% c("ESQUIZOFRENIA", "ESQUIZOTIPICO", "PSICOSIS") ~ "PSICOTICO",
pat_dual == "TDAH" ~ "TDAH",
pat_dual %in% c("TGA", "ANSIEDAD") ~ "TGA",
pat_dual %in% c("PTSD", "TEA INDUCIDO", "TCA") ~ "OTRO",
TRUE ~ pat_dual))
## rearrange columns
dflimpio <- dflimpio %>% select(fecha_ingreso, año, edad, rangos_edad, sexo, nacionalidad, estado_civil, nivel_educativo, condicion_laboral, convivencia, convivencia2, sustancia, sustancia2, pat_dual, pat_dual2)4 - Data analysis
4.1 - Demographics
4.1.1 - Year of admission
## Distribution of admission year according to the substance related to admission
table(dflimpio$sustancia2)##
## ALCOHOL CANNABIS
## 46 158
## COCAÍNA CRACK
## 64 23
## OPIOIDES OTRAS SUSTANCIA O ADICCIONES
## 29 17table(dflimpio$sustancia2, dflimpio$año)##
## 2019 2020 2021 2022
## ALCOHOL 7 6 19 14
## CANNABIS 21 33 50 54
## COCAÍNA 5 10 26 23
## CRACK 1 7 4 11
## OPIOIDES 6 4 7 12
## OTRAS SUSTANCIA O ADICCIONES 2 4 5 6prop.table(table(dflimpio$sustancia2, dflimpio$año), margin = 2)##
## 2019 2020 2021 2022
## ALCOHOL 0.16666667 0.09375000 0.17117117 0.11666667
## CANNABIS 0.50000000 0.51562500 0.45045045 0.45000000
## COCAÍNA 0.11904762 0.15625000 0.23423423 0.19166667
## CRACK 0.02380952 0.10937500 0.03603604 0.09166667
## OPIOIDES 0.14285714 0.06250000 0.06306306 0.10000000
## OTRAS SUSTANCIA O ADICCIONES 0.04761905 0.06250000 0.04504505 0.05000000chisq.test(table(dflimpio$sustancia2, dflimpio$año))##
## Pearson's Chi-squared test
##
## data: table(dflimpio$sustancia2, dflimpio$año)
## X-squared = 14.309, df = 15, p-value = 0.5022library(ggplot2)
qplot(edad, sustancia2, data = dflimpio, facets = .~ año)
The analysis of admission trends by substance reveals that cannabis consistently emerged as the leading substance associated with treatment admissions throughout the 2019–2022 period, accounting for approximately 44–51% of cases each year. Cocaine and alcohol followed, though with substantially lower percentages. Other substances—such as crack, opioids, and miscellaneous addictions—showed lower frequencies overall, with a slight increase in crack-related admissions noted in 2022.
A chi-square test (χ² = 14.228, p = 0.5083) found no statistically significant association between the year of admission and the substance involved. This indicates that the distribution of substances remained relatively stable over the years analyzed, suggesting consistent patterns in the types of substance use that prompted individuals to seek treatment or care during this period.
4.1.2 - Age
## Distribution of the substance related to admission according the age groups
table(dflimpio$rangos_edad, dflimpio$sustancia2)##
## ALCOHOL CANNABIS COCAÍNA CRACK OPIOIDES OTRAS SUSTANCIA O ADICCIONES
## 10-14 1 1 0 0 0 0
## 15-19 2 41 1 0 2 1
## 20-24 3 58 4 2 5 3
## 25-29 2 29 9 4 9 4
## 30-34 6 21 7 6 5 7
## 35-39 10 2 13 6 4 1
## 40-44 6 3 10 3 2 0
## 45-49 3 0 7 1 0 1
## 50-54 6 1 5 1 1 0
## 55-59 3 0 4 0 1 0
## 60-64 1 2 3 0 0 0
## 65-69 1 0 1 0 0 0
## 70-74 2 0 0 0 0 0prop.table(table(dflimpio$rangos_edad, dflimpio$sustancia2), margin = 2)##
## ALCOHOL CANNABIS COCAÍNA CRACK OPIOIDES
## 10-14 0.021739130 0.006329114 0.000000000 0.000000000 0.000000000
## 15-19 0.043478261 0.259493671 0.015625000 0.000000000 0.068965517
## 20-24 0.065217391 0.367088608 0.062500000 0.086956522 0.172413793
## 25-29 0.043478261 0.183544304 0.140625000 0.173913043 0.310344828
## 30-34 0.130434783 0.132911392 0.109375000 0.260869565 0.172413793
## 35-39 0.217391304 0.012658228 0.203125000 0.260869565 0.137931034
## 40-44 0.130434783 0.018987342 0.156250000 0.130434783 0.068965517
## 45-49 0.065217391 0.000000000 0.109375000 0.043478261 0.000000000
## 50-54 0.130434783 0.006329114 0.078125000 0.043478261 0.034482759
## 55-59 0.065217391 0.000000000 0.062500000 0.000000000 0.034482759
## 60-64 0.021739130 0.012658228 0.046875000 0.000000000 0.000000000
## 65-69 0.021739130 0.000000000 0.015625000 0.000000000 0.000000000
## 70-74 0.043478261 0.000000000 0.000000000 0.000000000 0.000000000
##
## OTRAS SUSTANCIA O ADICCIONES
## 10-14 0.000000000
## 15-19 0.058823529
## 20-24 0.176470588
## 25-29 0.235294118
## 30-34 0.411764706
## 35-39 0.058823529
## 40-44 0.000000000
## 45-49 0.058823529
## 50-54 0.000000000
## 55-59 0.000000000
## 60-64 0.000000000
## 65-69 0.000000000
## 70-74 0.000000000age_specific <- c("15-19", "20-24", "25-29")
specific_table <- table(dflimpio$rangos_edad, dflimpio$sustancia2)
total_specific <- sum(specific_table[age_specific, ])
total_cannabis <- sum(specific_table[age_specific, "CANNABIS"])
total_cannabis/total_specific## [1] 0.71508384.1.2.1 - Pearson’s Chi-square test
chisq.test(table(dflimpio$rangos_edad, dflimpio$sustancia2))##
## Pearson's Chi-squared test
##
## data: table(dflimpio$rangos_edad, dflimpio$sustancia2)
## X-squared = 197.75, df = 60, p-value < 2.2e-164.1.2.2- Histogram of the age distribution
# Load necessary libraries
library(lattice)
# Create new dataset
dist_edad <- dflimpio
# Create subsets for CANNABIS and Non-CANNABIS categories
dist_edad_cannabis <- subset(dist_edad, sustancia2 == "CANNABIS")
dist_edad_non_cannabis <- subset(dist_edad, sustancia2 != "CANNABIS")
# Calculate mean and median for each group
mean_all <- mean(dist_edad$edad)
median_all <- median(dist_edad$edad)
mean_cannabis <- mean(dist_edad_cannabis$edad)
median_cannabis <- median(dist_edad_cannabis$edad)
mean_non_cannabis <- mean(dist_edad_non_cannabis$edad)
median_non_cannabis <- median(dist_edad_non_cannabis$edad)
# Combine all data with an additional facet variable
dist_edad$facet <- "Overall"
dist_edad_cannabis$facet <- "CANNABIS"
dist_edad_non_cannabis$facet <- "Non-CANNABIS"
combined_data <- rbind(dist_edad, dist_edad_cannabis, dist_edad_non_cannabis)
# Create a panel function to add vertical lines for mean and median
panel_histogram <- function(x, ...) {
panel.histogram(x, ...)
panel.abline(v = median(x), col = "black", lwd = 3, lty = 2)
panel.abline(v = mean(x), col = "red", lwd = 3, lty = 1)
}
# Plot using lattice
histogram(~edad | facet, data = combined_data,
main = "Age distribution according to substance",
layout = c(1, 3),
col = "gray65",
panel = panel_histogram,
breaks = 35,
strip = strip.custom(factor.levels = c("Cannabis", "Non-Cannabis", "Entire Group")),
xlab = "Age", ylab = "Patients")
A combined analysis of age distribution and the primary substance associated with admission reveals a clear distinction between patients admitted for cannabis use and those admitted for other substances. As illustrated in the histogram, patients admitted for cannabis tend to be significantly younger, with a marked concentration between the ages of 15 and 24. In contrast, the non-cannabis group displays a more dispersed distribution, skewed toward older age ranges.
This pattern is further supported by tabulated data: 71% of cannabis-related admissions fall within the 15 to 29 age range, suggesting that cannabis use primarily affects a younger demographic. Moreover, the chi-square test yields a statistically significant result (χ² = 198.79, p < 0.001), indicating a strong association between age and the substance related to admission.
4.1.2.2 - 14-24 Age group focus
## Create new variable grupo_edad2 to focus on patients between 14 and 24 years
dflimpio <- dflimpio %>%
mutate(grupo_edad2 = case_when(
edad >= 14 & edad <= 24 ~ "14-24",
edad >= 25 ~ "25 o más",
TRUE ~ NA_character_
)) %>%
relocate(grupo_edad2, .after = rangos_edad)
## Distribution of the substance related to admission according the new groups
table(dflimpio$sustancia2, dflimpio$grupo_edad2)##
## 14-24 25 o más
## ALCOHOL 6 40
## CANNABIS 100 58
## COCAÍNA 5 59
## CRACK 2 21
## OPIOIDES 7 22
## OTRAS SUSTANCIA O ADICCIONES 4 13prop.table(table(dflimpio$sustancia2, dflimpio$grupo_edad2), margin = 2)##
## 14-24 25 o más
## ALCOHOL 0.04838710 0.18779343
## CANNABIS 0.80645161 0.27230047
## COCAÍNA 0.04032258 0.27699531
## CRACK 0.01612903 0.09859155
## OPIOIDES 0.05645161 0.10328638
## OTRAS SUSTANCIA O ADICCIONES 0.03225806 0.06103286A closer look at the 14 to 24 age group reveals that over 80% of admissions in this category were related to cannabis use (80.5%), reaffirming the distinctly youthful profile of problematic cannabis consumption. In contrast, substances such as alcohol, cocaine, crack, and opioids play a much larger role among individuals aged 25 and older, where the distribution is more balanced and diverse.
This pattern highlights cannabis as the primary substance leading to admission among younger individuals, whereas substance-related admissions among adults tend to be more varied. These findings are critical for designing age-specific prevention strategies—targeting youth-focused efforts on cannabis use prevention, while adopting broader, multi-component interventions for adults.
4.1.3 Sex, nationality, status
table(dflimpio$sexo, dflimpio$sustancia2)##
## ALCOHOL CANNABIS COCAÍNA CRACK OPIOIDES OTRAS SUSTANCIA O ADICCIONES
## F 12 27 9 3 6 2
## M 34 131 55 20 23 15table(dflimpio$nacionalidad)##
## AMERICANO COLOMBIANO CUBANO DOMINICANO ESPANOL ITALIANO MEXICANO
## 54 2 1 275 1 1 1
## UCRANIANO VENEZOLANO
## 1 1table(dflimpio$estado_civil)##
## CASADO DIVORCIADO SEPARADO SOLTERO UNION VIUDO
## 36 8 2 270 19 2An analysis of sociodemographic variables reveals that, in terms of sex, men represent a clear majority across all substance types—especially in cannabis (130 men vs. 27 women) and cocaine (54 men vs. 9 women) cases—suggesting a higher prevalence of problematic use among males.
Regarding nationality, the vast majority of admitted patients are Dominican (276 out of 337), followed distantly by U.S. nationals (53).
As for marital status, most patients are single (270 cases), which may be associated with psychosocial vulnerability or lower structural stability. This variable may also be key for designing targeted intervention strategies.
4.1.4 Education, employment, Household composition
table(dflimpio$nivel_educativo)##
## PRIMARIA SECUNDARIA UNIVERSITARIA
## 50 214 73table(dflimpio$condicion_laboral)##
## NO SI
## 211 126table(dflimpio$sustancia2, dflimpio$convivencia2)##
## ALGUN PADRE HIJOS OTRO PAREJA PAREJA E HIJOS
## ALCOHOL 15 2 1 12 2
## CANNABIS 119 0 16 13 0
## COCAÍNA 23 4 3 14 3
## CRACK 7 0 1 3 1
## OPIOIDES 20 0 2 2 0
## OTRAS SUSTANCIA O ADICCIONES 5 0 2 3 1
##
## SOLO
## ALCOHOL 14
## CANNABIS 10
## COCAÍNA 17
## CRACK 11
## OPIOIDES 5
## OTRAS SUSTANCIA O ADICCIONES 6The sociodemographic profile related to education, employment, and living arrangements highlights the social vulnerability of individuals admitted for substance use. Most patients reported having completed secondary education (213 individuals), followed by a significant group with university-level education (75), indicating that problematic substance use is not confined to lower educational backgrounds. Nevertheless, a relevant number of cases had only completed primary education (49), underscoring the diversity of educational levels among this population.
In terms of employment status, the majority were unemployed (207 compared to 130 who were employed), suggesting a possible link between unemployment and treatment admission—potentially due to functional impairment related to substance use or the structural exclusion these individuals may face in the labor market.
Finally, analysis of living arrangements by substance reveals that cannabis use is most commonly associated with individuals living with at least one parent (117 cases), reinforcing the youthful profile of these patients. In contrast, those admitted for cocaine or alcohol use show a more diverse distribution, with a notable presence of individuals living alone or with a partner. These patterns may offer key insights into the familial and social contexts surrounding substance use, and can help inform more targeted and context-sensitive therapeutic interventions.
4.2 - Dual diagnosis
table(dflimpio$sustancia2, dflimpio$pat_dual2)##
## ANTISOCIAL BIPOLAR DEPRESION LIMITE LUDOPATIA
## ALCOHOL 0 0 3 2 1
## CANNABIS 1 7 5 4 0
## COCAÍNA 1 3 2 1 2
## CRACK 0 2 0 0 1
## OPIOIDES 0 0 0 0 0
## OTRAS SUSTANCIA O ADICCIONES 0 1 1 1 0
##
## NO OTRO PSICOTICO TDAH TGA
## ALCOHOL 39 0 0 0 1
## CANNABIS 110 3 24 3 1
## COCAÍNA 55 0 0 0 0
## CRACK 19 0 1 0 0
## OPIOIDES 27 0 1 1 0
## OTRAS SUSTANCIA O ADICCIONES 13 0 1 0 0prop.table(table(dflimpio$pat_dual2, dflimpio$sustancia2))##
## ALCOHOL CANNABIS COCAÍNA CRACK OPIOIDES
## ANTISOCIAL 0.000000000 0.002967359 0.002967359 0.000000000 0.000000000
## BIPOLAR 0.000000000 0.020771513 0.008902077 0.005934718 0.000000000
## DEPRESION 0.008902077 0.014836795 0.005934718 0.000000000 0.000000000
## LIMITE 0.005934718 0.011869436 0.002967359 0.000000000 0.000000000
## LUDOPATIA 0.002967359 0.000000000 0.005934718 0.002967359 0.000000000
## NO 0.115727003 0.326409496 0.163204748 0.056379822 0.080118694
## OTRO 0.000000000 0.008902077 0.000000000 0.000000000 0.000000000
## PSICOTICO 0.000000000 0.071216617 0.000000000 0.002967359 0.002967359
## TDAH 0.000000000 0.008902077 0.000000000 0.000000000 0.002967359
## TGA 0.002967359 0.002967359 0.000000000 0.000000000 0.000000000
##
## OTRAS SUSTANCIA O ADICCIONES
## ANTISOCIAL 0.000000000
## BIPOLAR 0.002967359
## DEPRESION 0.002967359
## LIMITE 0.002967359
## LUDOPATIA 0.000000000
## NO 0.038575668
## OTRO 0.000000000
## PSICOTICO 0.002967359
## TDAH 0.000000000
## TGA 0.000000000psicosis <- table(dflimpio$pat_dual2, dflimpio$sustancia2)
total_psicosis <- sum(psicosis["PSICOTICO", ])
psicosis_cannabis <- psicosis["PSICOTICO", "CANNABIS"]
psicosis_cannabis/total_psicosis## [1] 0.8888889The analysis of dual diagnoses reveals that the vast majority of admitted patients did not present any co-occurring psychiatric disorder. However, the analysis also reveals a strong association between cannabis use and the presence of psychotic disorders. Among patients diagnosed with a psychotic condition, 88.9% were admitted due to cannabis use, suggesting a significant link between this substance and the onset or exacerbation of psychotic symptoms. This proportion is markedly higher than that associated with any other substance in this category.
Cannabis use also shows greater prevalence among patients diagnosed with bipolar disorder, ADHD, and borderline personality disorder, which may point to a more complex clinical profile among cannabis users. In contrast, the majority of patients admitted for other substances did not present a dual diagnosis—for instance, 54 out of 63 cocaine users and 40 out of 47 alcohol users had no additional psychiatric diagnosis.
These findings underscore the need for differentiated assessment and treatment protocols for cannabis users, particularly due to the elevated risk of psychotic episodes. They also highlight the importance of integrating comprehensive mental health care into substance use treatment programs.