Social Inequalities in Chronic Diseases (ER 1243)

Description

** Incidence and prevalence rates of chronic diseases by different sociodemographic variables (sex, age, region, tenth of standard of living, socio-professional group, diploma).** These statistics were produced for the publication “Chronic diseases more often affect modest people and further reduce their life expectancy” ([Studies and Results N°1243](https://drees.solidarites-sante.gouv.fr/publications-communique-de-presse/etudes-et-resultats/les-maladies-chroniques-touchent-plus-souvent)) where methodological choices are explained. R programs are available at [this address](https://gitlab.com/DREES_code/public/etudes/er1243). The various breakdowns can be found in the _varGroupage_ column and the corresponding values in the _valGroupage_ column: * **sex** (_SEXE_) * **age class of 10 years** (_classAge10_) * **region** (_FISC_REG_S_) * **tenth standard of living** (_FISC_NIVVIEM_E2015_S_moy_10_) * **socio-occupational group** (_EAR_GS_S_) * **diploma** (_EAR_DIPLR_S_) These breakdowns are also proposed by **region** and **sex** (column _varPartition_). For example, the rates per tenth of living standards for the Grand East region (official geographical code 44) with the filter ‘varPartition == “FISC_REG_S” & valPartition == “44” & varGroupage == “FISC_NIVVIEM_E2015_S_moy_10”. To keep only prevalence, add the filter ‘type == “prevalence”’ and for the tenth most modest ‘valGroupage == “1”’. The data contain **weighted numbers** (_weight1_ and _weightTot_), **unstandardised rates** (_txNonStand_), **standardised rates** using the **direct** method** (_txStandDir_) and **indirect** (_txStandIndir_) [[explainations on standardisation](https://santepublique.med.univ-tours.fr/wp-content/uploads/2016/07/methodes_standardisation.pdf)] as well as ** confidence intervals** at 95 % of all these rates (columns sufficient by _BB_ for low terminal and _BH_ for high terminals). Standardisations are made on the age group and sex and reference population is that obtained without grouping. When a partition is defined (region or gender), the reference population is that of the partition value (e.g. the population of the Grand East region, or the population of women). The diseases in question (column _varTauxLib_) are those of CNAM pathology mapping. See the publication for more details. Categories (e.g. cardioneurovascular diseases) as well as detail (e.g. “acute coronary syndrome”) are provided, with the exception of types of cancer that could not be reconstructed with sufficient quality. To understand the contents of the columns, scroll the “Data Template” below or hover over column names in the “Table” tab. For the terms of _valGroupage_ or _valPartition_, see the attached file below _libelles_er1243.xlsx_. An example of an R program using this data is offered in the attached file below _ex_analysis_chronic diseases.R_.

Resources

Name Format Description Link
54 https://www.data.gouv.fr/fr/datasets/r/815954b4-577e-4ed2-8aae-f29b8d6d0ec5
0 https://www.data.gouv.fr/api/1/datasets/r/b66200d9-9291-4b1b-ae61-3782f212b872
8 https://www.data.gouv.fr/api/1/datasets/r/dd89f77c-3575-49da-8b0c-2186133d2e0c
23 https://www.data.gouv.fr/api/1/datasets/r/8467a271-0b2f-4734-af53-65841bf16bd0
0 https://www.data.gouv.fr/api/1/datasets/r/aa7561fd-2751-4d39-979e-bcad1c333a04
0 https://www.data.gouv.fr/api/1/datasets/r/9d636680-048b-49e2-aa1c-18eac20be582

Tags

  • inegalite-sociale
  • sante-et-systeme-de-soins
  • maladie-chronique
  • maladie
  • disparites-et-inegalites-territoriales

Topics

Categories