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      COVID-19 Pandemic and Hospitalizations due to Abortion among 10- to 14-Year-Old Girls in Brazil

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          Abstract

          Introduction

          Various non-pharmacological interventions to prevent coronavirus dissemination were implemented during the COVID-19 pandemic, including school closures. The effect of these interventions on particular aspects of people’s lives such as sexual and reproductive health outcomes has not been adequately discussed. The objective of the study was to compare the monthly hospital admission rates due to abortion before and during school closure.

          Methods

          We used an interrupted time series (IES) design to estimate the hospital admission rates before and during the school closure (intervention in March 2020) period. The analysis was performed considering all girls from age groups of interest and by stratifying the age groups according to skin color (white and non-white) in which the non-white category comprised both the black and mixed ethnicity together. Coefficients and 95% confidence intervals (95% CIs) were calculated using segmented linear regression models.

          Results

          The results showed positive and statistically significant coefficients, suggesting post-intervention trend changes both in the population as a whole (coefficient: 0.07; 95% CI: 0.02; 0.11) and the non-white population group (coefficient: 0.07; 95% CI: 0.03; 0.11), indicating that the monthly hospital admission rates increased over the post-intervention period compared to baseline pre-intervention period. The ITS analysis did not detect statistically significant trend changes (coefficient: 0.02; 95% CI: −0.01; 0.05) in abortion admission rates in the white girl population group.

          Conclusion

          The hospitalizations in Brazil due to abortions in 10- to 14-year-old girls increased during the COVID-19 pandemic in 2020 compared to 2019, and the number of abortions was higher in the non-white population than the white population. Furthermore, recognizing that the implementation of school closure has affected the minority population differentially can help develop more effective actions to face other future similar situations.

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          Most cited references37

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          Interrupted time series regression for the evaluation of public health interventions: a tutorial

          Abstract Interrupted time series (ITS) analysis is a valuable study design for evaluating the effectiveness of population-level health interventions that have been implemented at a clearly defined point in time. It is increasingly being used to evaluate the effectiveness of interventions ranging from clinical therapy to national public health legislation. Whereas the design shares many properties of regression-based approaches in other epidemiological studies, there are a range of unique features of time series data that require additional methodological considerations. In this tutorial we use a worked example to demonstrate a robust approach to ITS analysis using segmented regression. We begin by describing the design and considering when ITS is an appropriate design choice. We then discuss the essential, yet often omitted, step of proposing the impact model a priori. Subsequently, we demonstrate the approach to statistical analysis including the main segmented regression model. Finally we describe the main methodological issues associated with ITS analysis: over-dispersion of time series data, autocorrelation, adjusting for seasonal trends and controlling for time-varying confounders, and we also outline some of the more complex design adaptations that can be used to strengthen the basic ITS design.
            • Record: found
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            • Article: not found

            Segmented regression analysis of interrupted time series studies in medication use research.

            Interrupted time series design is the strongest, quasi-experimental approach for evaluating longitudinal effects of interventions. Segmented regression analysis is a powerful statistical method for estimating intervention effects in interrupted time series studies. In this paper, we show how segmented regression analysis can be used to evaluate policy and educational interventions intended to improve the quality of medication use and/or contain costs.
              • Record: found
              • Abstract: not found
              • Article: not found

              Segmented regression analysis of interrupted time series studies in medication use research

                Author and article information

                Journal
                Port J Public Health
                Port J Public Health
                PJP
                PJP
                Portuguese Journal of Public Health
                S. Karger AG (Basel, Switzerland )
                2504-3137
                2504-3145
                4 March 2024
                April 2024
                : 42
                : 1
                : 23-32
                Affiliations
                [a ]Faculdade de Medicina, Departamento de Cirurgia Geral e Especializada, Universidade Federal Fluminense, Niterói, Brazil
                [b ]Faculdade de Medicina. Universidade Federal Fluminense, Niterói, Brazil
                [c ]Faculdade de Economia, Contábeis e Atuária, Pontifícia Universidade Católica de São Paulo, São Paulo, Brazil
                Author notes
                Correspondence to: Billy McBenedict, bmcbenedict@ 123456id.uff.br
                Article
                535227
                10.1159/000535227
                11499666
                6c04d261-8f47-4b31-9e82-119dbd897c29
                © 2024 The Author(s). Published by S. Karger AG, Basel on behalf of NOVA National School of Public Health

                This article is licensed under the Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC) ( http://www.karger.com/Services/OpenAccessLicense). Usage and distribution for commercial purposes requires written permission.

                History
                : 26 October 2022
                : 7 November 2023
                : 2024
                Page count
                Figures: 4, Tables: 1, References: 28, Pages: 10
                Funding
                This study did not receive financial support.
                Categories
                Research Article

                covid-19: abortion,adolescent,hospitalization,interrupted time series analysis,epidemiology,health surveillance,reproductive health

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