[TCRED-SANTE-PERSONNELS] Health personnel
Updated by provider on October 24, 2023 (12:00 AM).
[FREQ] Frequency
- [A] Annual
[INDICATEUR] Indicator
- [DENSITE_METIERS] Density of professions
- [NOMBRE_MEDECINS] Number of medical practitioners
[NATURE] Nature
- [RATIO] Ratio
- [VALEUR_ABSOLUE] Absolute value
[METIER] Professions
- [CHD] Dental surgeons
- [IDE] State-certified nursing staff
- [MED] Medical practitioners
- [MEDG] General medical practitioners
- [MEDS] Specialist medical practitioners
- [MEDSG] Specialist and general medical practitioners
- [MK] Masseur-kinesitherapists
- [PH] Pharmacists
[REF_AREA] Reference area
- [D01] 01 - Ain
- [D02] 02 - Aisne
- [D03] 03 - Allier
- [D04] 04 - Alpes-de-Haute-Provence
- [D05] 05 - Hautes-Alpes
- [D06] 06 - Alpes-Maritimes
- [D07] 07 - Ardèche
- [D08] 08 - Ardennes
- [D09] 09 - Ariège
- [D10] 10 - Aube
- [D11] 11 - Aude
- [D12] 12 - Aveyron
- [D13] 13 - Bouches-du-Rhône
- [D14] 14 - Calvados
- [D15] 15 - Cantal
- [D16] 16 - Charente
- [D17] 17 - Charente-Maritime
- [D18] 18 - Cher
- [D19] 19 - Corrèze
- [D21] 21 - Côte-d'Or
- [D22] 22 - Côtes-d'Armor
- [D23] 23 - Creuse
- [D24] 24 - Dordogne
- [D25] 25 - Doubs
- [D26] 26 - Drôme
- [D27] 27 - Eure
- [D28] 28 - Eure-et-Loir
- [D29] 29 - Finistère
- [D2A] 2A - Corse-du-Sud
- [D2B] 2B - Haute-Corse
- [D30] 30 - Gard
- [D31] 31 - Haute-Garonne
- [D32] 32 - Gers
- [D33] 33 - Gironde
- [D34] 34 - Hérault
- [D35] 35 - Ille-et-Vilaine
- [D36] 36 - Indre
- [D37] 37 - Indre-et-Loire
- [D38] 38 - Isère
- [D39] 39 - Jura
- [D40] 40 - Landes
- [D41] 41 - Loir-et-Cher
- [D42] 42 - Loire
- [D43] 43 - Haute-Loire
- [D44] 44 - Loire-Atlantique
- [D45] 45 - Loiret
- [D46] 46 - Lot
- [D47] 47 - Lot-et-Garonne
- [D48] 48 - Lozère
- [D49] 49 - Maine-et-Loire
- [D50] 50 - Manche
- [D51] 51 - Marne
- [D52] 52 - Haute-Marne
- [D53] 53 - Mayenne
- [D54] 54 - Meurthe-et-Moselle
- [D55] 55 - Meuse
- [D56] 56 - Morbihan
- [D57] 57 - Moselle
- [D58] 58 - Nièvre
- [D59] 59 - Nord
- [D60] 60 - Oise
- [D61] 61 - Orne
- [D62] 62 - Pas-de-Calais
- [D63] 63 - Puy-de-Dôme
- [D64] 64 - Pyrénées-Atlantiques
- [D65] 65 - Hautes-Pyrénées
- [D66] 66 - Pyrénées-Orientales
- [D67] 67 - Bas-Rhin
- [D68] 68 - Haut-Rhin
- [D69] 69 - Rhône
- [D70] 70 - Haute-Saône
- [D71] 71 - Saône-et-Loire
- [D72] 72 - Sarthe
- [D73] 73 - Savoie
- [D74] 74 - Haute-Savoie
- [D75] 75 - Paris
- [D76] 76 - Seine-Maritime
- [D77] 77 - Seine-et-Marne
- [D78] 78 - Yvelines
- [D79] 79 - Deux-Sèvres
- [D80] 80 - Somme
- [D81] 81 - Tarn
- [D82] 82 - Tarn-et-Garonne
- [D83] 83 - Var
- [D84] 84 - Vaucluse
- [D85] 85 - Vendée
- [D86] 86 - Vienne
- [D87] 87 - Haute-Vienne
- [D88] 88 - Vosges
- [D89] 89 - Yonne
- [D90] 90 - Territoire de Belfort
- [D91] 91 - Essonne
- [D92] 92 - Hauts-de-Seine
- [D93] 93 - Seine-Saint-Denis
- [D94] 94 - Val-de-Marne
- [D95] 95 - Val-d'Oise
- [D971] 971 - Guadeloupe
- [D972] 972 - Martinique
- [D973] 973 - French Guiana
- [D974] 974 - La Réunion
- [D976] 976 - Mayotte
- [FE] France
- [FM] Metropolitan France
- [F_H_IDF] Metropolitan France excluding Île-de-France
- [R11] Île-de-France
- [R24] Centre-Val de Loire
- [R27] Bourgogne-Franche-Comté
- [R28] Normandie
- [R32] Hauts-de-France
- [R44] Grand Est
- [R52] Pays de la Loire
- [R53] Bretagne
- [R75] Nouvelle-Aquitaine
- [R76] Occitanie
- [R84] Auvergne-Rhône-Alpes
- [R93] Provence-Alpes-Côte d'Azur
- [R94] Corse
[UNIT_MEASURE] Unit
- [INDIVIDUS] individuals
[CORRECTION] Correction
- [BRUT] Uncorrected
[BASIND] Base period
- [2012] 2012
- [2017] 2017
- [SO] Not applicable
[SERIE_ARRETEE] Stopped series
- [FALSE] nO
- [TRUE] yes
Search filters
Frequency [FREQ] (1)
Indicator [INDICATEUR] (2)
Nature [NATURE] (2)
Professions [METIER] (8)
Reference area [REF_AREA] (117)
Unit [UNIT_MEASURE] (1)
Correction [CORRECTION] (1)
Base period [BASIND] (3)
Stopped series [SERIE_ARRETEE] (2)
This dataset has 1,989 series:
| Series code | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2018 | 2019 | 2020 | 2021 | 2022 | 2023 | 2007 | 2008 | 2009 | 2010 | 2011 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| [A.DENSITE_METIERS.RATIO.CHD.D11.INDIVIDUS.BRUT.SO.FALSE] | NA | NA | 61 | 62 | 61 | 61 | 61 | 62 | 62 | 63 | 61 | 63 | NA | NA | NA | NA | NA |
| [A.DENSITE_METIERS.RATIO.CHD.D11.INDIVIDUS.BRUT.SO.TRUE] | 60 | NA | 61 | 62 | 61 | 61 | 63 | NA | NA | NA | NA | NA | 61 | 61 | 58 | 59 | NA |
| [A.DENSITE_METIERS.RATIO.CHD.D12.INDIVIDUS.BRUT.SO.FALSE] | NA | NA | 62 | 63 | 61 | 61 | 61 | 60 | 58 | 55 | 57 | 57 | NA | NA | NA | NA | NA |
| [A.DENSITE_METIERS.RATIO.CHD.D12.INDIVIDUS.BRUT.SO.TRUE] | 64 | NA | 62 | 63 | 61 | 61 | 61 | NA | NA | NA | NA | NA | 67 | 64 | 64 | 64 | NA |
| [A.DENSITE_METIERS.RATIO.CHD.D13.INDIVIDUS.BRUT.SO.FALSE] | NA | NA | 85 | 86 | 86 | 87 | 87 | 88 | 89 | 89 | 92 | 96 | NA | NA | NA | NA | NA |
| [A.DENSITE_METIERS.RATIO.CHD.D13.INDIVIDUS.BRUT.SO.TRUE] | 86 | NA | 85 | 87 | 86 | 88 | 88 | NA | NA | NA | NA | NA | 88 | 88 | 88 | 88 | NA |
| [A.DENSITE_METIERS.RATIO.CHD.D14.INDIVIDUS.BRUT.SO.FALSE] | NA | NA | 45 | 44 | 46 | 47 | 46 | 46 | 47 | 48 | 50 | 52 | NA | NA | NA | NA | NA |
| [A.DENSITE_METIERS.RATIO.CHD.D14.INDIVIDUS.BRUT.SO.TRUE] | 46 | NA | 45 | 44 | 46 | 47 | 47 | NA | NA | NA | NA | NA | 45 | 45 | 45 | 46 | NA |
| [A.DENSITE_METIERS.RATIO.CHD.D15.INDIVIDUS.BRUT.SO.FALSE] | NA | NA | 55 | 53 | 53 | 52 | 52 | 52 | 51 | 51 | 50 | 44 | NA | NA | NA | NA | NA |
| [A.DENSITE_METIERS.RATIO.CHD.D15.INDIVIDUS.BRUT.SO.TRUE] | 55 | NA | 55 | 53 | 55 | 54 | 53 | NA | NA | NA | NA | NA | 54 | 54 | 53 | 53 | NA |
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