Risk of Acute Myocardial Infarction in Pneumoconiosis: Results from a Retrospective Cohort Study

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Abstract

Background: Pneumoconiosis (PCN) has several comorbidities, most notably pulmonary and cardiovascular diseases. However, much is still unknown about the relationship between PCN and acute myocardial infarction (AMI). The present study aimed to clarify the association between PCN and subsequent AMI risk using a retrospective cohort study design. Methods: This was a population-based, retrospective cohort study that used data from Taiwan’s National Health Insurance Database. A total of 7556 newly diagnosed patients with PCN and 7556 individuals without PCN were included in the PCN and comparison cohort (PC and CC), respectively, between 2008 and 2018, with propensity score matching for age, gender, comorbidity, medication, and date of PCN diagnosis. The occurrence of AMI was monitored until the end of 2019, and AMI risk was assessed using Cox proportional hazard regression models. Results: The overall incidence of AMI was 1.34-fold higher in the PC than in the CC (4.33 vs. 3.23 per 1000 person-years, respectively, p < 0.05), with an adjusted hazard ratio (aHR) of 1.36 (95% confidence interval (CI): 1.08–1.72) after controlling for age, gender, comorbidity, and medication. Further analyses showed a higher risk of AMI with increased annual number of emergency department visits among patients with PCN (aHR: 1.30, 95% CI: 1.01–1.66 (<1) and aHR: 1.68, 95% CI: 1.13–2.50 (≥1)). Conclusion: Patients with PCN had a significantly higher risk of developing AMI than those without PCN. Clinicians should pay more attention to prevent AMI episodes in patients with PCN.

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Taiwan’s National Health Insurance Research Database: past and future

Cheng-Yang Hsieh, Chien-Chou Su, Shih-Chieh Shao … (2019)

Abstract Taiwan’s National Health Insurance Research Database (NHIRD) exemplifies a population-level data source for generating real-world evidence to support clinical decisions and health care policy-making. Like with all claims databases, there have been some validity concerns of studies using the NHIRD, such as the accuracy of diagnosis codes and issues around unmeasured confounders. Endeavors to validate diagnosed codes or to develop methodologic approaches to address unmeasured confounders have largely increased the reliability of NHIRD studies. Recently, Taiwan’s Ministry of Health and Welfare (MOHW) established a Health and Welfare Data Center (HWDC), a data repository site that centralizes the NHIRD and about 70 other health-related databases for data management and analyses. To strengthen the protection of data privacy, investigators are required to conduct on-site analysis at an HWDC through remote connection to MOHW servers. Although the tight regulation of this on-site analysis has led to inconvenience for analysts and has increased time and costs required for research, the HWDC has created opportunities for enriched dimensions of study by linking across the NHIRD and other databases. In the near future, researchers will have greater opportunity to distill knowledge from the NHIRD linked to hospital-based electronic medical records databases containing unstructured patient-level information by using artificial intelligence techniques, including machine learning and natural language processes. We believe that NHIRD with multiple data sources could represent a powerful research engine with enriched dimensions and could serve as a guiding light for real-world evidence-based medicine in Taiwan.