Background: Advances in information technology have paved the way to facilitate accessibility to population-level health data through web-based data query systems (WDQSs). Despite these advances in technology, US state agencies face many challenges related to the dissemination of their local health data. It is essential for the public to have access to high-quality data that are easy to interpret, reliable, and trusted. These challenges have been at the forefront throughout the COVID-19 pandemic. Objective: The purpose of this study is to identify the most significant challenges faced by state agencies, from the perspective of the Behavioral Risk Factor Surveillance System (BRFSS) coordinator from each state, and to assess if the coordinators from states with a WDQS perceive these challenges differently. Methods: We surveyed BRFSS coordinators (N=43) across all 50 US states and the District of Columbia. We surveyed the participants about contextual factors and asked them to rate system aspects and challenges they faced with their health data system on a Likert scale. We used two-sample t tests to compare the means of the ratings by participants from states with and without a WDQS. Results: Overall, 41/43 states (95%) make health data available over the internet, while 65% (28/43) employ a WDQS. States with a WDQS reported greater challenges (P=.01) related to the cost of hardware and software (mean score 3.44/4, 95% CI 3.09-3.78) than states without a WDQS (mean score 2.63/4, 95% CI 2.25-3.00). The system aspect of standardization of vocabulary scored more favorably (P=.01) in states with a WDQS (mean score 3.32/5, 95% CI 2.94-3.69) than in states without a WDQS (mean score 2.85/5, 95% CI 2.47-3.22). Conclusions: Securing of adequate resources and commitment to standardization are vital in the dissemination of local-level health data. Factors such as receiving data in a timely manner, privacy, and political opposition are less significant barriers than anticipated.

Query systems were recently introduced as an architecture for constructing compilers, and have shown to enable fast and efficient incremental compilation, where results from previous builds is reused to accelerate future builds. With this architecture, a compiler is composed of several queries, each of which extracts a small piece of information about the source program. For example, one query might determine the type of a variable, and another the list of functions defined in some file. The dependencies of a query, which includes other queries or files on disk, are automatically recorded at runtime. With these dependencies, query systems can detect changes in their inputs and incorporate them into the final output, while reusing old results from queries which have not changed. This reduces the amount of work needed to recompile code, which saves both time and energy. We present a new parallel execution model for query systems using work-stealing, which dynamically balances the workload across multiple threads. This is facilitated by various augmentations to existing algorithms to allow concurrent operations. Furthermore, we introduce a novel data structure that accelerates incremental compilation for common use cases. We evaluated the impact of these augmentations by implementing a compiler frontend capable of parsing and type-checking the Go programming language. We demonstrate a 10x reduction in compile times using the parallel execution mode. Finally, under certain common conditions, we show a 5x reduction in incremental compile times compared to the state-of-the-art. / Query-system är en ny arkitektur som har använts för att implementera kompilatorer för programspråk och har ett fokus på att möjliggöra snabb och effektiv inkrementell kompilering. Med denna arkitektur består en kompilator flera olika mindre funktioner, som var och en svarar på en liten fråga om källprogrammet, såsom typen av en variabel eller listan över funktioner i en fil. Genom att spåra hur dessa funktioner anropar varandra, och den data de läser, kan kompilatorer upptäcka förändringar i sina indata och utföra den minimala mängd arbete som krävs för att sammanställa dessa förändringar i utdata. Detta minskar mängden arbete som behövs för att kompilera om kod, vilket sparar både tid och energi. I denna rapport presenterar vi en ny exekveringsmodell för Query-system som möjliggör parallellism med hjälp av work-stealing. Detta underlättas av flera tillägg till befintliga algoritmer som gör det möjligt att utföra alla operationer parallellt. Utöver detta introducerar vi även en ny datastruktur som gör inkrementell kompilering snabbare för många vanliga användningsområden. Vi utvärderade effekten av dessa förändringar genom att implementera ett kompilatorgränssnitt som kan analysera och verifiera korrekthet av typer Go-programmeringsspråket. Resultaten visar en 10x reduktion i kompileringstider med hjälp av parallellkörningsläget. Vi demonstrerar även 5 gånger lägre kompileringstider vid inkrementella ändringar än vad som tidigare varit möjligt.