Perspectives on the Future of Epidemiology: A Framework for Training

Algorithm for forming hospital care episodes by combining attendance contacts in the Danish National Patient Register: A methodological consensus-driven study

BACKGROUND

Studying complete hospital care episodes from register data, for instance when assessing length of stay, discharges and readmissions, can cause methodological difficulties due to the lack of a contact linkage identifier. We aimed to develop an algorithm combining sequential attendance contacts in the Danish National Patient Register (DNPR) into hospital care episodes, spanning the entire duration and all contacts from hospital arrival to departure.

METHODS

The algorithm was developed under the consensus of experts from research institutions across Denmark. It reads in second and third version DNPR data, deletes contacts without attendance, duplicates elective outpatient contacts corresponding to attendance dates and modifies contact types (e.g. repeated acute contacts), among others. Thereafter, sequential contacts within 4 h are marked as the same hospital care episode, consisting of one or more DNPR contact. We tested the algorithm in a data set of adults living in Denmark during 2013-2021 and compared different hourly cut-offs.

RESULTS

For the demonstration, we included 120.2 m contacts from 5.7 m persons, combined into 105.9 m hospital care episodes. Of the hospital care episodes, 6.4% were acute inpatients, 8.3% were acute outpatients, 2.0% were elective inpatients and 83.3% were elective outpatients. Using 4 h as our recommendation, 3-h, 5-h and 6-h cut-offs for contact combining revealed only minor differences in the number of hospital care episodes (<0.4%), whereas 12-h (<1.7%) and 24-h cut-offs (<43.1%) had a larger impact.

CONCLUSIONS

The algorithm automates data reading, modification and linkage of sequential attendance contacts. The algorithm can be initiated as a SAS macro and is available from an online repository.

Identifying skills required of new epidemiologists: a content analysis of Canadian job postings and master's programs

Introduction

The rise of emerging public health threats has increased the need for qualified epidemiologists in Canada. Our study aimed to identify the knowledge, skills, and abilities (KSAs) required of epidemiologists entering the workforce and determine whether these align with those taught in graduate epidemiology programs.

Methods

An inductive content analysis of Canadian job postings from May to December 2023 containing the keyword "epidemiology" and requiring master's degrees in epidemiology or related fields was conducted to identify the KSAs required in the workforce. Inductive content analysis of Master of Science (MSc) program descriptions and core course descriptions was completed to discern skills gained through Canadian graduate epidemiology and public health programs.

Results

Based on the 295 job postings analyzed, five KSA categories were identified: communication skills (n = 268, 90.8%), analytical skills (n = 267, 90.5%), soft skills (n = 254, 86.1%), research methodology (n = 217, 73.6%), and knowledge of epidemiological concepts (n = 170, 57.6%). Analysis of 18 MSc programs found that that all of them described analytical skills, research methodology, and epidemiological concepts within their curriculum. Communication skills were described in 94.4% (n = 17) of programs, while soft skills were mentioned in 50.0% (n = 9). However, only 66.7% (n = 12) of programs outlined learning objectives or specified the skills acquired from their programs in their descriptions.

Conclusion

There was alignment between the needs of the Canadian epidemiology job market and MSc programs, particularly in analytical skills and research methodology. However, development of soft skills should be emphasized within graduate epidemiology programs to better prepare graduates for the job market. Future research should aim to develop competency statements for epidemiologists in training to ensure consistency across graduate programs and promote career readiness.

An effectiveness evaluation of a community-based course for medical students: a randomized controlled trial in the teaching of epidemiology

BACKGROUND

Epidemiology is considered to be the fundamental science of public health and plays an important role in clinical competence and professional development. The objective of this study is to evaluate the effectiveness of a short-term course for the teaching of epidemiology, which was designed as a community-based class for medical students.

METHOD

This course was designed according to Kern's six-step approach to curriculum development. A total of 75 undergraduates were recruited. Forty-one students were assigned to an experimental group engaged in theoretical teaching and practical courses, while 34 students were assigned to the control group only taking theoretical courses. All participants were asked to complete a pre- and post-course survey and to take a test after completing the course. The scores between the experimental and control groups were compared using the Wilcoxon test.

RESULT

The experimental group showed significantly higher self-assessment scores in course understanding (p = 0.0126) and clinical practice skills (p = 0.0005) after completing the course, while no significant difference was observed in the control group. In addition, students in the experimental group reported significantly higher interest (p = 0.0015), stronger learning motivation (p = 0.0113) and a better mastery of epidemiology (p = 0.0167) after completing the course than those in the control group. However, test scores (p = 0.0859) and pass rates (p = 0.1755) demonstrated no statistical significance between the two groups.

CONCLUSION

The short-term practical course in epidemiology exerted significantly positive effects on the improvement of student learning enthusiasm, course understanding and clinical practice skills. These findings provide new ideas and statistical evidence for the development of epidemiological instruction. Future studies should explore how to more widely and optimally apply community-based courses to the teaching of epidemiology.

On the Need to Revitalize Descriptive Epidemiology

Nearly every introductory epidemiology course begins with a focus on person, place, and time, the key components of descriptive epidemiology. And yet in our experience, introductory epidemiology courses were the last time we spent any significant amount of training time focused on descriptive epidemiology. This gave us the impression that descriptive epidemiology does not suffer from bias and is less impactful than causal epidemiology. Descriptive epidemiology may also suffer from a lack of prestige in academia and may be more difficult to fund. We believe this does a disservice to the field and slows progress towards goals of improving population health and ensuring equity in health. The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) outbreak and subsequent coronavirus disease 2019 pandemic have highlighted the importance of descriptive epidemiology in responding to serious public health crises. In this commentary, we make the case for renewed focus on the importance of descriptive epidemiology in the epidemiology curriculum using SARS-CoV-2 as a motivating example. The framework for error we use in etiological research can be applied in descriptive research to focus on both systematic and random error. We use the current pandemic to illustrate differences between causal and descriptive epidemiology and areas where descriptive epidemiology can have an important impact.

Is the Way Forward to Step Back? Documenting the Frequency with which Study Goals are Misaligned with Study Methods and Interpretations in the Epidemiologic Literature

In any research study, there is an underlying research process that should begin with a clear articulation of the study's goal. The study's goal drives this process; it determines many study features including the estimand of interest, the analytic approaches that can be used to estimate it, and which coefficients, if any, should be interpreted. "Misalignment" can occur in this process when analytic approaches and/or interpretations do not match the study's goal; misalignment is potentially more likely to arise when study goals are ambiguously framed. This study documented misalignment in the observational epidemiologic literature and explored how the framing of study goals contributes to its occurrence. The following misalignments were examined: 1) use of an inappropriate variable selection approach for the goal (a "goal-methods" misalignment) and 2) interpretation of coefficients of variables for which causal considerations were not made (e.g., Table 2 Fallacy, a "goal-interpretation" misalignment). A random sample of 100 articles published 2014-2018 in the top 5 general epidemiology journals were reviewed. Most reviewed studies were causal, with either explicitly stated (13/103, 13%) or associationally-framed (71/103, 69%) aims. Full alignment of goal-methods-interpretations was infrequent (9/103, 9%), although clearly causal studies (5/13, 38%) were more often fully aligned than seemingly causal ones (3/71, 4%). Goal-methods misalignments were common (34/103, 33%), but most frequently, methods were insufficiently reported to draw conclusions (47/103, 46%). Goal-interpretations misalignments occurred in 31% (32/103) of studies and occurred less often when the methods were aligned (2/103, 2%) compared with when the methods were misaligned (13/103, 13%).