Evaluation of cardiovascular disease risk factors with Propensity Score Matching and Coarsened Exact Matching: Nepalese post-seismic observational data

• Autor(a) principal: Adrega, Tiago
• Data de Publicação: 2021
• Outros Autores: Rocha, Anabela, Miranda, M. Cristina
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
• Texto Completo: http://hdl.handle.net/10773/32821
• topic: Cardiovascular disease risk factors; Propensity score matching; Health behaviours; Coarsened exact matching; Observational studies

With observational data, an important step of the research process is skipped, resulting in some restrictions to make inferences concerning the treatment effects. Some methodologies have been developed in order to reduce the imbalance in the samples of treated and control units. Propensity Score Matching (PSM) is still one of the most common approaches applied but coarsened Exact Matching (CEM) appears to produce better results, most of the times in which it is used. This work illustrates the application of each of the two techniques to a set of data from the Nepal population. Our aim is to compare the two methodologies and evaluate in what way their use adds information about the prevalence of Cardio Vascular Disease (CVD) risk. Data refers to a remote village population that was separated into two groups after the incidents of the May 2015 earthquake. The study was carried out during a humanitarian mission in Nepal, aimed to provide medical care to the people of Sindhupalchok, a northern Nepalese region, with approximately 1200inhabitants. With the seismic event, this population got separated into groups of dislodged individuals: victims that stayed nearby village areas and those who went towards Kathmandu looking for support in temporary settlements. Both these populations were supported by the medical mission. Cross-sectional data were collected approximately 18 months after the earthquake and included demographic data, anthropometric data, previous medical history, CVD risk factors, and health behaviors. The assessment of CVD risk factors and health behaviours was based on a question-by-question guide provided by the WHO. In order to compare both approaches, we computed two imbalance measures, L1 and Percent Bias Reduction (PBR). The results show that CEM dominates PSM. From the application of the two approaches, we find that the results are generally in agreement but CEM methodology allowed to highlight some data features not seen before with PSM.