Calculation of sample size is an essential part of research study design since it affects the reliability and feasibility of the research study. In this article, we look at the principles of sample size calculation for different types of research studies.

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In evidence-based medicine frameworks, the highest level of evidence is derived from quantitative synthesis of double-masked, high-quality, randomly assigned controlled trials. Meta-analyses of randomly assigned controlled trials have demonstrated that screening mammography reduces breast cancer deaths. In the United States, every major guideline-producing organization has recommended screening mammography in average-risk women; however, there are controversies about age and frequency. Carefully controlled observational research studies and statistical modeling studies can address evidence gaps and inform evidence-based, contemporary screening practices. As breast imaging radiologists develop and evaluate existing and new screening tests and technologies, they will need to understand the key methodological considerations and scientific criteria used by policy makers and health service researchers to support dissemination and implementation of evidence-based screening tests. The Wilson and Jungner principles and the U.S. Preventive Services Task Force general analytic framework provide structured evaluations of the effectiveness of screening tests. Key considerations in both frameworks include public health significance, natural history of disease, cost-effectiveness, and characteristics of screening tests and treatments. Rigorous evaluation of screening tests using analytic frameworks can maximize the benefits of screening tests while reducing potential harms. The purpose of this article is to review key methodological considerations and analytic frameworks used to evaluate screening studies and develop evidence-based recommendations.

We conducted retrospective public health surveillance using data from 2006 to 2016 in seven integrated delivery systems from FDA\'s Sentinel System. We identified pediatric hypertensive patients by clinical and claims-based definitions and compared demographics, baseline profiles and follow-up time profiles. Among 3,757,803 pediatric patients aged 3 to 17 years, we identified 781,722 children and 551,246 teens with at least three blood pressure measures over 36-months. Of these, 70,315 children (9%) and 47,928 teens (8.7%) met the clinical definition for hypertension and 22,465 (2.8%) children and 60,952 (11%) of teens met the clinical definition for elevated, non-hypertensive blood pressure. Of the 3.7M patients, we identified 3,246 children and 7,293 teens with any claim for hypertension (claims definition). Evidence of hypertension claims among those meeting our clinical definition was poor; 2.2% and 7.3% of clinically hypertensive children and teens had corresponding claims for hypertension. Baseline profiles for claims-based hypertensive patients suggest greater severity of disease compared to clinical patients. Claims-based patients showed higher rates of all-cause mortality during follow-up. Pediatric hypertension in claims-based data sources is under-captured but may serve as a marker for greater disease severity. Investigators should understand coding practices when selecting real-world data sources for future pediatric hypertension work.

Deep learning is a subfield of artificial intelligence and machine learning based mostly on neural networks and often combined with attention algorithms that has been used to detect and identify objects in text, audio, images, and video. Serghiou and Rough (Am J Epidemiol. 0000;000(00):0000-0000) present a primer for epidemiologists on deep learning models. These models provide substantial opportunities for epidemiologists to expand and amplify their research in both data collection and analyses by increasing the geographic reach of studies, including more research subjects, and working with large or high dimensional data. The tools for implementing deep learning methods are not quite yet as straightforward or ubiquitous for epidemiologists as traditional regression methods found in standard statistical software, but there are exciting opportunities for interdisciplinary collaboration with deep learning experts, just as epidemiologists have with statisticians, healthcare providers, urban planners, and other professionals. Despite the novelty of these methods, epidemiological principles of assessing bias, study design, interpretation and others still apply when implementing deep learning methods or assessing the findings of studies that have used them.

This paper presents causal loop diagrams (CLDs) as tools for studying complex public health problems like health inequality. These problems often involve feedback loops-a characteristic of complex systems not fully integrated into mainstream epidemiology. CLDs are conceptual models that visualize connections between system variables. They are commonly developed through literature reviews or participatory methods with stakeholder groups. These diagrams often uncover feedback loops among variables across scales (e.g. biological, psychological and social), facilitating cross-disciplinary insights. We illustrate their use through a case example involving the feedback loop between sleep problems and depressive symptoms. We outline a typical step-by-step process for developing CLDs in epidemiology. These steps are defining a specific problem, identifying the key system variables involved, mapping these variables and analysing the CLD to find new insights and possible intervention targets. Throughout this process, we suggest triangulating between diverse sources of evidence, including domain knowledge, scientific literature and empirical data. CLDs can also be evaluated to guide policy changes and future research by revealing knowledge gaps. Finally, CLDs may be iteratively refined as new evidence emerges. We advocate for more widespread use of complex systems tools, like CLDs, in epidemiology to better understand and address complex public health problems.

Hypertension is a common \"silent killer\" in adult medicine, but epidemiologic estimates of elevated blood pressure in children and adolescents are challenged by under-diagnosis and resultant low utilization of relevant administrative or billing codes. In the article by Horgan et al (Am J Epidemiol 2024), children and adolescents with hypertension and elevated blood pressure were identified using direct assessment of blood pressure measurements available in the electronic health record from both inpatient and outpatient visits (\"clinical cohort\") in comparison to diagnosis codes (\"claims-based cohort\"). The study population included 3.75 million pediatric healthcare visits available in the US Food and Drug Administration\'s Sentinel System. While the study applied a relatively novel methodology to interrogate available clinical data within the EHR to better understand the prevalence of pediatric hypertension and raised concern for a higher occurrence of hypertension among children and adolescents than previously realized using claims codes, the utility of the prevalence estimates may be limited by the potential for misclassification bias inherent in EHR data. However, these data raise important concerns about relaying solely on ICD-9-CM/ICD-10-CM codes to quantify the epidemiology of pediatric hypertension and highlight opportunities to address elevated blood pressure in children that could improve long-term cardiovascular health.

Electronic medical records (EMR) are important for rapidly compiling information to determine disease characteristics (e.g., symptoms) and risk factors (e.g., underlying comorbidities, medications) for disease-related outcomes. To assess EMR data accuracy, agreement between EMR abstractions and patient interviews was evaluated. Symptoms, medical history, and medication usage among COVID-19 patients collected from EMR and patient interviews were compared using overall agreement (same answer in EMR and interview), reported agreement (yes answer in both EMR and interview among those who reported yes in either), and Kappa statistics. Overall, patients reported more symptoms in interviews than in EMR abstractions. Overall agreement was high (≥50% for 20/23 symptoms), but only subjective fever and dyspnea had reported agreement of ≥50%. Kappa statistics for symptoms were generally low. Reported medical conditions had greater agreement with all condition categories (10/10) having ≥50% overall agreement and half (5/10) having ≥50% reported agreement. More non-prescription medications were reported in interviews than in EMR abstractions leading to low reported agreement (28%). Discordance was observed for symptoms, medical history, and medication usage between EMR abstractions and patient interviews. Investigations utilizing EMR to describe clinical characteristics and identify risk factors should consider the potential for incomplete data, particularly for symptoms and medications.

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BACKGROUND: Surveillance of healthcare-associated infections (HAIs) in Australia is disparate, resource intensive, unsustainable, and provides limited information. Traditional HAI surveillance is time intensive and agreement levels between clinicians have been shown to be variable.
OBJECTIVE: To compare two methods: a semi-automated algorithm, and coding data, against traditional surgical site infection (SSI) surveillance methods.
METHODS: This retrospective multi-centre cohort study included all patients undergoing a hip (HPRO) or knee (KPRO) prosthesis and coronary artery bypass graft (CABG) surgery during a two-year period at two large metropolitan hospitals. Routine SSI data were obtained via the infection prevention and control (IPC) team, a previously developed algorithm was applied to all patient records, and the ICD-10-AM data were searched for those categorized as having an SSI.
RESULTS: Overall, 1447, 1416, and 1026 patients who underwent HPRO, KPRO, and CABG, respectively, were included. The highest sensitivity values were generated by the algorithm: HPRO deep or organ-space (D/O) 0.87 (95% confidence interval: 0.66-0.96), CABG 0.86 (0.64-0.96), and HPRO all SSI 0.77 (0.57-89); the lowest sensitivity was Code CABG D/O 0.03 (0.00-0.21). The highest PPV values were generated by the algorithm: HPRO D/O 0.97 (0.77-0.99), CABG D/O 0.97 (0.76-0.99), and the Code HPRO D/O 0.9 (0.66-0.99). Both the algorithm and coding data resulted in a substantial reduction in the number of medical records required to review.
CONCLUSIONS: The application of algorithms to enhance SSI surveillance demonstrates high accuracy in identifying patient records that require review by IPC teams to determine the presence of an SSI. Coding data alone should not be used to identify SSIs.