A patient safety knowledge graph supporting vaccine product development.

BACKGROUND: Knowledge graphs are well-suited for modeling complex, unstructured, and multi-source data and facilitating their analysis. During the COVID-19 pandemic, adverse event data were integrated into a knowledge graph to support vaccine safety surveillance and nimbly respond to urgent health authority questions. Here, we provide details of this post-marketing safety system using public data sources. In addition to challenges with varied data representations, adverse event reporting on the COVID-19 vaccines generated an unprecedented volume of data; an order of magnitude larger than adverse events for all previous vaccines. The Patient Safety Knowledge Graph (PSKG) is a robust data store to accommodate the volume of adverse event data and harmonize primary surveillance data sources. METHODS: We designed a semantic model to represent key safety concepts. We built an extract-transform-load (ETL) data pipeline to parse and import primary public data sources; align key elements such as vaccine names; integrated the Medical Dictionary for Regulatory Activities (MedDRA); and applied quality metrics. PSKG is deployed in a Neo4J graph database, and made available via a web interface and Application Programming Interfaces (APIs). RESULTS: We import and align adverse event data and vaccine exposure data from 250 countries on a weekly basis, producing a graph with 4,340,980 nodes and 30,544,475 edges as of July 1, 2022. PSKG is used for ad-hoc analyses and periodic reporting for several widely available COVID-19 vaccines. Analysis code using the knowledge graph is 80% shorter than an equivalent implementation written entirely in Python, and runs over 200 times faster. CONCLUSIONS: Organizing safety data into a concise model of nodes, properties, and edge relationships has greatly simplified analysis code by removing complex parsing and transformation algorithms from individual analyses and instead managing these centrally. The adoption of the knowledge graph transformed how the team answers key scientific and medical questions. Whereas previously an analysis would involve aggregating and transforming primary datasets from scratch to answer a specific question, the team can now iterate easily and respond as quickly as requests evolve (e.g., "Produce vaccine-X safety profile for adverse event-Y by country instead of age-range").

Factors and Predictors of Health Related Quality of Life of the General Population of Pakistan.

Background and Objective: The standards of living, improvement in public health, and medical care in Pakistan are increasing day by day, health-related quality of life (HRQoL) has been increasingly acknowledged in various patient's reported outcomes in Pakistan. However, a large-scale general population-based study on assessing HQRoL in Pakistan was not conducted. Therefore, this study aimed to evaluate HRQoL for the general Pakistani population. Material and Methods: A cross-sectional study with a population sample (n = 16,672) was selected from all Pakistan provinces using a stratified sampling approach. The EQ-5D-3L tool was used to measure the HRQoL of the general population of Pakistan. The descriptive and inferential statistics have been done by using SPSS version 20. Results: Overall, 121 health states were reported in this study. EQ-5D index and EQ-VAS scores were 0.74 ± 0.32 and 0.75 ± 0.25, respectively. The percentage of people responding to any problems increased with age. Males have better health as compared to females in all age groups. All demographics were significantly associated (P < 0.01) with the mean EQ5D index and VAS scores except residence (p > 0.05). The regression model reported that age was the best predictor of the EQ-5D index scores after adjusting for the covariates (beta = 0.19; p < 0.001). This study provides Pakistani population HRQoL data measured by the EQ-5D tool, based on a national representative sample. Conclusion: The current study concluded that Age, City, Gender, Education, Occupation, Residence, and House occupancy are significantly affecting HRQOL. The socioeconomically deprived groups and females have inferior health status than more advantaged. The trends detected in high-income nations were usually similar to Pakistan.