1 dataset found
  1. Study data.xlsx

    • figshare.com
    xlsx
    Updated May 31, 2023
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Gabriela Maria Reis Goncalves (2023). Study data.xlsx [Dataset]. http://doi.org/10.6084/m9.figshare.6982991.v1
    Explore at:
    xlsxAvailable download formats
    Dataset updated
    May 31, 2023
    Dataset provided by
    Figsharehttp://figshare.com/
    Authors
    Gabriela Maria Reis Goncalves
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    A top-down approach was adopted for the identification, measurement, and valuation of the direct costs of CKD and ESKD, based on administrative data obtained from the SIA/SUS and SIH/SUS. These systems serve as a registry of all procedures reimbursed by the Brazilian Ministry of Health to health facilities (hospitals, clinics, and laboratories), public or private, that provide services to the Unified Health System. Direct costs were defined as those of outpatient (SIA/SUS) and inpatient (SIH/SUS) procedures, such as doctor’s appointments, laboratory tests, medications, hospital admissions, treatment of complications, renal replacement therapy, and renal transplantation. Non-medical direct costs (patient transport, caregiver payment), indirect costs (absenteeism, presenteeism, and early death), and intangible costs (loss of ability to work, loss of quality of life, among others) were disregarded.

    For analysis, direct costs were stratified by variable (sex, race/skin color, and age) and combined with International Classification of Diseases (ICD-10) codes for CKD (N18, N18.8, N18.9) and ESKD (N18.0). The variables sex, race/skin color, and age were divided into the following categories: a) sex – female and male; b) age – 18 to 29 years, 30 to 59 years, 60 to 64 years, 65 to 74 years, and >75 years; c) race/skin color – yellow (Asian), white, red (Native Brazilian), mix-race (pardo), black, and not reported. Specifically for the race/skin color variable, data reallocation was required, as 24% of database entries belonged to the “not reported” category. Data reallocation was performed by calculating the percent share of the categories “yellow”, “white”, “red”, “mix-race”, “black” in relation to total cost, disregarding costs for the “not reported” category; then, costs for the “not reported” category were redistributed based on the percent share calculated in the previous step. Nominal values ​​for the 2010-2016 period were used, without any adjustment for inflation.

  2. Not seeing a result you expected?
    Learn how you can add new datasets to our index.

Share
FacebookFacebook
TwitterTwitter
Email
Click to copy link
Link copied
Close
Cite
Gabriela Maria Reis Goncalves (2023). Study data.xlsx [Dataset]. http://doi.org/10.6084/m9.figshare.6982991.v1
Organization logo

Study data.xlsx

Explore at:
39 scholarly articles cite this dataset (View in Google Scholar)
xlsxAvailable download formats
Dataset updated
May 31, 2023
Dataset provided by
Figsharehttp://figshare.com/
Authors
Gabriela Maria Reis Goncalves
License

Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically

Description

A top-down approach was adopted for the identification, measurement, and valuation of the direct costs of CKD and ESKD, based on administrative data obtained from the SIA/SUS and SIH/SUS. These systems serve as a registry of all procedures reimbursed by the Brazilian Ministry of Health to health facilities (hospitals, clinics, and laboratories), public or private, that provide services to the Unified Health System. Direct costs were defined as those of outpatient (SIA/SUS) and inpatient (SIH/SUS) procedures, such as doctor’s appointments, laboratory tests, medications, hospital admissions, treatment of complications, renal replacement therapy, and renal transplantation. Non-medical direct costs (patient transport, caregiver payment), indirect costs (absenteeism, presenteeism, and early death), and intangible costs (loss of ability to work, loss of quality of life, among others) were disregarded.

For analysis, direct costs were stratified by variable (sex, race/skin color, and age) and combined with International Classification of Diseases (ICD-10) codes for CKD (N18, N18.8, N18.9) and ESKD (N18.0). The variables sex, race/skin color, and age were divided into the following categories: a) sex – female and male; b) age – 18 to 29 years, 30 to 59 years, 60 to 64 years, 65 to 74 years, and >75 years; c) race/skin color – yellow (Asian), white, red (Native Brazilian), mix-race (pardo), black, and not reported. Specifically for the race/skin color variable, data reallocation was required, as 24% of database entries belonged to the “not reported” category. Data reallocation was performed by calculating the percent share of the categories “yellow”, “white”, “red”, “mix-race”, “black” in relation to total cost, disregarding costs for the “not reported” category; then, costs for the “not reported” category were redistributed based on the percent share calculated in the previous step. Nominal values ​​for the 2010-2016 period were used, without any adjustment for inflation.

Search
Clear search
Close search
Google apps
Main menu