Last data update: 21 sept. 2026, 15:49
Last compile: 21 sept. 2026, 15:51
Structure
Prévalence et incidence des grandes catégories de maladies chroniques
Code
mc_cat |>
filter(is.na(varpartition), vargroupage == "classeAge10") |>
group_by(type, catlib) |>
summarise(taux = weighted.mean(txstanddir, poids1), .groups = "drop") |>
ggplot() +
geom_col(aes(x = reorder(catlib, taux), y = taux, fill = type), position = "dodge") +
coord_flip() +
theme_minimal() + xlab("") + ylab("Taux standardisé (France entière)") +
scale_y_continuous(labels = percent_format(accuracy = 0.1)) +
scale_fill_viridis_d() +
theme(legend.position = "bottom", legend.title = element_blank())
Un fort gradient social : prévalence selon le niveau de vie
Code
mc_cat |>
filter(type == "prevalence", is.na(varpartition), vargroupage == "FISC_NIVVIEM_E2015_S_moy_10") |>
mutate(decile = as.integer(valgroupage)) |>
ggplot() +
geom_line(aes(x = decile, y = txstanddir, color = catlib)) +
geom_point(aes(x = decile, y = txstanddir, color = catlib), size = 0.8) +
facet_wrap(~catlib, scales = "free_y", labeller = labeller(catlib = scales::label_wrap(20))) +
theme_minimal() + xlab("Décile de niveau de vie (1 = plus modeste)") + ylab("Prévalence standardisée") +
scale_x_continuous(breaks = 1:10) +
scale_y_continuous(labels = percent_format(accuracy = 0.1)) +
scale_color_viridis_d() +
theme(legend.position = "none",
strip.text = element_text(size = 7.5),
axis.text.x = element_text(size = 6))
Prévalence selon le sexe
Code
mc_cat |>
filter(type == "prevalence", is.na(varpartition), vargroupage == "SEXE") |>
mutate(sexe = recode(valgroupage, F = "Femmes", M = "Hommes")) |>
ggplot() +
geom_col(aes(x = reorder(catlib, txstanddir), y = txstanddir, fill = sexe), position = "dodge") +
coord_flip() +
theme_minimal() + xlab("") + ylab("Prévalence standardisée") +
scale_y_continuous(labels = percent_format(accuracy = 0.1)) +
scale_fill_viridis_d() +
theme(legend.position = "bottom", legend.title = element_blank())
Prévalence selon l’âge
Code
mc_cat |>
filter(type == "prevalence", is.na(varpartition), vargroupage == "classeAge10") |>
mutate(classe_age = factor(valgroupage, levels = age_levels)) |>
ggplot() +
geom_line(aes(x = classe_age, y = txstanddir, color = catlib, group = catlib)) +
theme_minimal() + xlab("") + ylab("Prévalence standardisée") +
scale_y_continuous(labels = percent_format(accuracy = 0.1)) +
scale_color_viridis_d() +
theme(legend.position = "bottom", legend.title = element_blank()) +
guides(color = guide_legend(nrow = 4))
Disparités régionales : le cas du diabète
Code
mc_cat |>
filter(type == "prevalence", vartauxlib == "Diabète", varpartition == "FISC_REG_S", is.na(vargroupage)) |>
mutate(region = recode(valpartition, !!!reg_labels)) |>
ggplot() +
geom_col(aes(x = reorder(region, txstanddir), y = txstanddir)) +
coord_flip() +
theme_minimal() + xlab("") + ylab("Prévalence standardisée du diabète") +
scale_y_continuous(labels = percent_format(accuracy = 0.1))