BMC medical research methodology · 2025
Use of quasi-experimental studies to evaluate causal effects of public health interventions in Portugal: a scoping review.
СтатьяScoping ReviewНазвание и аннотация: Перевод пока не подготовлен
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Leite A, Kislaya I, Machado A, Aguiar P, Nunes B, Dias C Matias
Аннотация
BACKGROUND: Quasi-experimental designs are a valid option to assess causal effects of public health interventions when randomized studies are unfeasible, but not widely used in Portugal. We identified and reviewed characteristics of studies employing quasi-experimental designs to evaluate causal effects of public health interventions in Portugal.
METHODS: PubMed, Scopus, Web of Science and CINHAL were searched, alongside grey literature, reference mining and contact of authors of eligible studies. We extracted information on the intervention assessed, study design, outcomes assessed, statistical analysis and reporting guidelines.
RESULTS: We identified 1143 studies; 25 were eligible. Studies assessed interventions in various areas, mainly healthcare services (28.0%), drugs/tobacco consumption policy (20.0%), and COVID-19 related restrictions (20.0%). Studies employed interrupted time series (56.0%) and difference-in-differences designs (44.0%). Analyses utilised regression-based models, namely linear (48.0%), negative binominal (20.0%) and logistic (12.0%). Studies analysed 53 outcomes, with two outcomes per study on average. No reporting guidelines were mentioned.
CONCLUSIONS: There is a limited number of studies using quasi-experimental designs to estimate the causal effects of public health interventions in Portugal, mainly interrupted time series and difference-in-differences. Training in this area might promote the adequate use and dissemination of quasi-experimental studies.
METHODS: PubMed, Scopus, Web of Science and CINHAL were searched, alongside grey literature, reference mining and contact of authors of eligible studies. We extracted information on the intervention assessed, study design, outcomes assessed, statistical analysis and reporting guidelines.
RESULTS: We identified 1143 studies; 25 were eligible. Studies assessed interventions in various areas, mainly healthcare services (28.0%), drugs/tobacco consumption policy (20.0%), and COVID-19 related restrictions (20.0%). Studies employed interrupted time series (56.0%) and difference-in-differences designs (44.0%). Analyses utilised regression-based models, namely linear (48.0%), negative binominal (20.0%) and logistic (12.0%). Studies analysed 53 outcomes, with two outcomes per study on average. No reporting guidelines were mentioned.
CONCLUSIONS: There is a limited number of studies using quasi-experimental designs to estimate the causal effects of public health interventions in Portugal, mainly interrupted time series and difference-in-differences. Training in this area might promote the adequate use and dissemination of quasi-experimental studies.