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Mental Health Care Services Provided by Age, Sex, Service Type, and Diagnosis in Bogota, Colombia, 2019-2023
Dataset   Open access

Mental Health Care Services Provided by Age, Sex, Service Type, and Diagnosis in Bogota, Colombia, 2019-2023

Alex Quistberg, Olga Lucia Sarmiento and Natalia Hoyos Botero
2025
DOI: https://doi.org/10.7910/dvn/fpqli7
url
https://doi.org/10.7910/dvn/fpqli7View
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Abstract

Colombia Computer and Information Science Earth and Environmental Sciences Healthcare Latin America Medicine, Health and Life Sciences Mental Health Social Sciences Wellbeing
This dataset is part of the ESCALA (Study of Urban Health and Climate Change in Informal Settlements in Latin America) project that was funded by the Lacuna Fund of the Meridian Institute https://lacunafund.org/. This dataset contains aggregated counts of mental health services by age, sex, year, service type, and diagnosis for Bogota, Colombia, 2019-2023. Data were provided by the RIPS (Spanish acronym for "Individual Records of Service Provision") and consolidated from SISPRO (Spanish acronym for "Comprehensive Social Protection Information System") - Ministry of Health and Social Protection. The data were organized and published on the portal saludata.saludcapital.gov.co and openly published on datosabiertos.bogota.gov.co. Each row in the database represents the count of care services, not the count of unique individuals served. Therefore, it is not possible to calculate the total number of individuals served by summing the partial values obtained at different levels of disaggregation. This is because the same person may be included in different groups within the same period if any of their attributes change over time. Data cleaning included: (1) Initially, two databases are obtained: one covering the period from 2019 to 2021 and another from 2022 to 2024 (up to August). First, both databases are unified, retaining only the columns they have in common. (2) Since the data are not disaggregated by month, the 2024 records are removed as they provide an unofficial count for all months, which could lead to errors during use. (3) Empty rows and reports outside Bogotá are removed. (4) Finally, each variable is adjusted by assigning the names established in the data dictionary and categorizing them according to the defined domains.

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