Sepsis in Latvia-Incidence, Outcomes, and Healthcare Utilization: A Retrospective, Observational Study.

This study explores the incidence, outcomes, and healthcare resource utilization concerning sepsis in Latvia's adult population. Using a merged database from the National Health Service and the Latvian Centre for Disease Prevention and Control, sepsis-related hospitalizations were analyzed from 2015-2020. Findings revealed a 53.1% surge in sepsis cases from 2015-2018 with subsequent stabilization. This spike was more prominent among elderly patients. The age/sex adjusted case fatality rate rose from 34.7% in 2015 to 40.5% in 2020. Of the 7764 sepsis survivors, the one-year mortality rate was 12% compared to 2.2% in a reference group of 20,686 patients with infections but no further signs of sepsis. Sepsis survivors also incurred higher healthcare costs, driven by longer rehospitalizations and increased pharmaceutical needs, though they accessed outpatient services less frequently than the reference group. These findings underscore the growing detection of sepsis in Latvia, with survivors facing poorer outcomes and suggesting the need for enhanced post-sepsis outpatient care.

Comparative Analysis of the Most Important Cardiovascular Risk Factors Based on Cross-Sectional Studies in the Population of Latvia.

Background and Objectives: The aim of the study was to analyze the prevalence of cardiovascular risk factors (RFs) in Latvia from the population-based cross-sectional study performed in 2019−2020 and to compare the results with a similar study done in 2009−2010. Materials and Methods: The target sample of 6000 individuals representing a cross-section of Latvia's inhabitants (aged 25−74) was formed using stratified two-stage cluster sampling. The survey had two components: (1) an interview using a pre-specified questionnaire and (2) physical examination (height, weight, arterial pressure) and collection of venous blood samples to measure levels of fasting glucose (Glu), total cholesterol (TC), high and low-density lipoprotein cholesterol (HDL-C/LDL-C), and triglycerides (Tg). In total, 4070 individuals were interviewed (32% non-response), from which 2218 (55%) individuals underwent physical examination and collection of blood samples. Results: The most frequently observed RFs were high LDL-C (62.0%), smoking (45.3%), and arterial hypertension (36.8%), while the prevalence of self-reported high cholesterol and hypertension was 19.3 and 18.6%, respectively. A decrease in the prevalence of hypertension, high LDL-C, and Glu was noted. Smoking decreased in younger men. The mean number of five most important cardiovascular RFs was 2.0 (95% confidence interval (CI) 2.0, 2.1); 2.3 (95% CI 2.2, 2.4) for men and 1.8 (95% CI 1.7, 19) for women. The average number of RFs has decreased by 0.3 in 10 years, t(5883) = −7.2, p < 0.001. Conclusions: Although the prevalence of cardiovascular RFs remains noteworthy, an improvement in the risk profile of the Latvian population has been observed over the past decade. The study shows subjective self-underestimation of cardiovascular risk.

The Impact of Prescribed Dosage Assumptions on the Evaluation of Adherence and Persistence to Medication in Patients After Acute Myocardial Infarction.

Medication adherence is a significant problem in public health. Prescription-level pharmacy databases have great potential for monitoring actual drug adherence patterns at the healthcare system level. Many research papers have reported adherence estimates in different settings and populations. However, comparison between studies is not always straightforward due to different approaches taken when computing adherence. A crucial component to accurately estimate adherence is the availability of days' supply information for each dispensing event. Reasonable assumptions regarding medication dosage have to be made, when this information is not available. In this study, we evaluate adherence and persistence to medication in patients after myocardial infarction and show that corresponding estimates differ significantly, when using different dosage assumptions, namely, when using defined daily dose or tablet per day dosage regimens. Moreover, we demonstrate that observed differences between medications might be a result of inaccurate dosage assumptions. We propose a comparison of distribution of days between dispensing events to that of days' supplied as a relatively simple visual inspection to validate dosage assumptions.

Legal and Ethical Issues in Secondary Use of Administrative Health Data: The Case of Latvian Healthcare Monitoring Datalink.

The paper presents analysis of the legal and ethical issues surrounding establishment of the Latvian Healthcare Monitoring Datalink. The paper covers three interconnected issues in the context of the use of administrative health data for research purposes - anonymization of data, concept of 'public interest' and involvement of research ethics committees. The analysis has been put into broader context of interaction between General Data Protection Regulation (GDPR), national legislative measures and practical needs of researchers. Neither GDPR, nor Latvian legal framework regulate the particularities on the use of potentially identifiable health data in research. Also, the practical use of 'public interest' as a basis for lawful processing of personal data concerning health for research purposes is not clear. More extended involvement of research ethics committees might serve as useful tool for determination the 'public interest' and for the evaluation of proportionality when balancing the aims of the research and the personal data protection.

Model-based query language for analyzing clinical processes.

Nowadays large databases of clinical process data exist in hospitals. However, these data are rarely used in full scope. In order to perform queries on hospital processes, one must either choose from the predefined queries or develop queries using MS Excel-type software system, which is not always a trivial task. In this paper we propose a new query language for analyzing clinical processes that is easily perceptible also by non-IT professionals. We develop this language based on a process modeling language which is also described in this paper. Prototypes of both languages have already been verified using real examples from hospitals.

Graphical modeling and query language for hospitals.

BACKGROUND: So far there has been little evidence that implementation of the health information technologies (HIT) is leading to health care cost savings. One of the reasons for this lack of impact by the HIT likely lies in the complexity of the business process ownership in the hospitals. The goal of our research is to develop a business model-based method for hospital use which would allow doctors to retrieve directly the ad-hoc information from various hospital databases. METHODS: We have developed a special domain-specific process modelling language called the MedMod. Formally, we define the MedMod language as a profile on UML Class diagrams, but we also demonstrate it on examples, where we explain the semantics of all its elements informally. Moreover, we have developed the Process Query Language (PQL) that is based on MedMod process definition language. The purpose of PQL is to allow a doctor querying (filtering) runtime data of hospital's processes described using MedMod. RESULTS: The MedMod language tries to overcome deficiencies in existing process modeling languages, allowing to specify the loosely-defined sequence of the steps to be performed in the clinical process. The main advantages of PQL are in two main areas - usability and efficiency. They are: 1) the view on data through "glasses" of familiar process, 2) the simple and easy-to-perceive means of setting filtering conditions require no more expertise than using spreadsheet applications, 3) the dynamic response to each step in construction of the complete query that shortens the learning curve greatly and reduces the error rate, and 4) the selected means of filtering and data retrieving allows to execute queries in O(n) time regarding the size of the dataset. CONCLUSIONS: We are about to continue developing this project with three further steps. First, we are planning to develop user-friendly graphical editors for the MedMod process modeling and query languages. The second step is to do evaluation of usability the proposed language and tool involving the physicians from several hospitals in Latvia and working with real data from these hospitals. Our third step is to develop an efficient implementation of the query language.