| Author | Coeli, Claudia Medina | |
| Author | Domingues, Rosa Maria Soares Madeira | |
| Author | Meijinhos, Lana | |
| Author | Bastos, Daniela Medina Coeli | |
| Author | Pinheiro, Rejane Sobrino | |
| Author | Saraceni, Valeria | |
| Author | Dias, Marcos Augusto Bastos | |
| Author | Paiva, Natália Santana | |
| Author | Camargo Jr, Kenneth Rochel de | |
| Access date | 2025-04-10T23:25:05Z | |
| Available date | 2025-04-10T23:25:05Z | |
| Document date | 2025 | |
| Citation | COELI, Claudia Medina et al. Using a deterministic matching computer routine to identify hospital episodes in a Brazilian de-identified administrative database for the analysis of obstetrics hospitalisations. International Journal of Population Data Science, v. 10, n. 1, p. 1-14, Mar. 2025. | en_US |
| ISSN | 2399-4908 | en_US |
| URI | https://www.arca.fiocruz.br/handle/icict/69557 | |
| Language | eng | en_US |
| Publisher | Swansea University | en_US |
| Rights | open access | en_US |
| Title | Using a deterministic matching computer routine to identify hospital episodes in a Brazilian de-identified administrative database for the analysis of obstetrics hospitalisations | en_US |
| Type | Article | en_US |
| DOI | 10.23889/ijpds.v10i1.2467 | |
| Abstract | Introduction: The absence of a unique patient identifier in the Brazilian hospital administrative database prevents the identification of hospital episodes with multiple hospitalisations of the same patient. Objectives: This study aims to evaluate the information gain by using a computer routine to identify acute Obstetrics hospital episodes and its impact on assessing marks of case severity. Methods: The data source was a de-identified Brazilian hospital administrative database from 2017 to 2020, including hospitalisations records of women of reproductive age (10 to 49 years old) for treating acute conditions (N=16,087,490). We processed this database by combining C++ and Python routines to create a hospital episodes database. From the latter, we selected obstetrics hospital episodes from 2018 to 2019 (N = 4,926,877). We compared selected characteristics of the hospital episodes according to their type (multiple vs single records per episode), testing for differences using effect size measures. We compared relative differences in case severity marks when using the hospital episode as the unit of analysis to that of isolated hospitalisations (N = 5,018,350). Results: Compared to single-record episodes, multiple-records episodes had longer length of stay, higher amount reimbursed, and lower proportion of discharge alive. When comparing isolated hospitalisations to hospital episodes analysis, we observed an increase in all case severity indicators, especially for hospital deaths, with an increment of 13.15%. The computer routine decreased the hospital admissions with a reason for hospital discharge that did not indicate the outcome (hospital stay or inter-hospital transfer) from 2.29% to 0.73. Conclusions: The deterministic matching computer routine proved valuable for identifying records that refer to the same hospital episode, which improved the assessment of severe cases. | en_US |
| Affilliation | Universidade Federal do Rio de Janeiro. Instituto de Estudos em Saúde Coletiva. Rio de Janeiro, RJ, Brasil. | en_US |
| Affilliation | Fundação Oswaldo Cruz. Instituto Nacional de Infectologia Evandro Chagas. Laboratório de Pesquisa Clínica em DST e AIDS. Rio de Janeiro, RJ, Brasil. | en_US |
| Affilliation | Secretaria Municipal de Saúde do Rio de Janeiro. Rio de Janeiro, RJ, Brasil. | en_US |
| Affilliation | Universidade Federal do Rio de Janeiro. Instituto de Estudos em Saúde Coletiva. Rio de Janeiro, RJ, Brasil. | en_US |
| Affilliation | Universidade Federal do Rio de Janeiro. Instituto de Estudos em Saúde Coletiva. Rio de Janeiro, RJ, Brasil. | en_US |
| Affilliation | Secretaria Municipal de Saúde do Rio de Janeiro. Rio de Janeiro, RJ, Brasil. | en_US |
| Affilliation | Fundação Oswaldo Cruz. Instituto Nacional de Saúde da Mulher, da Criança e do Adolescente Fernandes Figueira. Rio de Janeiro, RJ, Brasil. | en_US |
| Affilliation | Universidade Federal do Rio de Janeiro. Instituto de Estudos em Saúde Coletiva. Rio de Janeiro, RJ, Brasil. | en_US |
| Affilliation | Universidade do Estado do Rio de Janeiro. Instituto de Medicina Social. Rio de Janeiro, RJ, Brasil. | en_US |
| Subject | Hospitalization | en_US |
| Subject | Health administrative data | en_US |
| Subject | Obstetrics | en_US |
| Subject | Record linkage | en_US |
| Subject | Computer applications software | en_US |
| e-ISSN | 2399-4908 | |
