Clinical Public Health Research Articles

Evaluation of Navigating a Foodborne Outbreak: Medical Learners Before and During COVID-19.

Navigating a Foodborne Outbreak: Preparation for Interprofessional Practice is an interactive, competency-based, online educational module demonstrating interprofessional practice among health professionals to improve and protect population health, in the context of a foodborne outbreak. Authors reviewed registration data, pre- and post- module knowledge assessments, and module evaluations from the 978 medical students and physicians ("medical learners") who completed the module from July 2018-June 2021, comparing their outcomes as well as assessing potential impacts of the COVID-19 pandemic. Analyses were conducted in 2022 and 2024. Students' use remained stable, while physicians' use increased over time, primarily as part of MPH curricula. Pre-module knowledge assessment scores were higher for physicians. Average medical student performance improved by 51.37%; average physician performance improved by 33.33%. Pre-module questions that addressed the coordination of clinical and public health practices, governmental public health emergency response processes, and how clinicians can inform the response were challenging for physicians and may represent public and population health (PPH) content that has not been emphasized in medical curricula. Medical learners reported high satisfaction with the module. Medical students' agreement with statements about the relevance of the module to their current or future responsibilities and awareness of "how clinical care and public health systems work together to protect the health of populations" increased during the pandemic. The module successfully improved knowledge of clinical-public health collaborations to protect population health and is one model of how to integrate PPH content, including physicians' roles in the public health system, into medical education.

Leveraging artificial intelligence to optimize COVID-19 robust spread and vaccination roll-out strategies in Southern Africa.

The outbreak of coronavirus in the year 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) prompted widespread illness, death, and extended economic devastation worldwide. In response, numerous countries, including Botswana and South Africa, instituted various clinical public health (CPH) strategies to mitigate and control the disease. However, the emergence of variants of concern (VOC), vaccine hesitancy, morbidity, inadequate and inequitable vaccine supply, and ineffective vaccine roll-out strategies caused continuous disruption of essential services. Based on Botswana and South Africa hospitalization and mortality data, we studied the impact of age and gender on disease severity. Comparative analysis was performed between the two countries to establish a vaccination strategy that could complement the existing CPH strategies. To optimize the vaccination roll-out strategy, artificial intelligence was used to identify the population groups in need of insufficient vaccines. We found that COVID-19 was associated with several comorbidities. However, hypertension and diabetes were more severe and common in both countries. The elderly population aged ≥60 years had 70% of major COVID-19 comorbidities; thus, they should be prioritized for vaccination. Moreover, we found that the Botswana and South Africa populations had similar COVID-19 mortality rates. Hence, our findings should be extended to the rest of Southern African countries since the population in this region have similar demographic and disease characteristics.

Defining Clinical Public Health

To solve complex health issues, an innovative and multidisciplinary framework is necessary. The Clinical Public Health (CPH) Division was established at the University of Toronto (UofT), Canada to foster inte-gration of primary care, preventive medicine and public health in education, practice and research. To better understand how the construct of CPH might be applied, we surveyed clinicians, researchers and public health professionals affiliated with the CPH Division to assess their understanding of the CPH concept and its utility in fostering broad collaboration. A two-wave anonymous survey of the active faculty of the CPH Division, UofT was conducted across Canada. Wave 1 participants (n = 187; 2016) were asked to define CPH, while Wave 2 participants (n = 192; 2017) were provided a synthesis of Wave 1 results and asked to rank each definition. Both waves were asked about the need for a common definition, and to comment on CPH. Response rates for the first and second waves were 25% and 22%, respectively. Of the six definitions of CPH from Wave 1, "the intersection of clinical practice and public health," was most highly ranked by Wave 2 participants. Positive perceptions of CPH included multidisciplinary collaboration, new fields and insights, forward thinking and innovation. Negative perceptions included CPH being a confusing term, too narrow in scope or too clinical. The concept of Clinical Public Health can foster multidisciplinary collaboration to address com-plex health issues because it provides a useful framework for bringing together key disciplines and diverse professional specialties.

Leveraging Artificial Intelligence and Big Data to Optimize COVID-19 Clinical Public Health and Vaccination Roll-Out Strategies in Africa

COVID-19 is imposing massive health, social and economic costs. While many developed countries have started vaccinating, most African nations are waiting for vaccine stocks to be allocated, and are using clinical public health (CPH) strategies to control the pandemic. The emergence of Variants of Concern (VOC), unequal access to the vaccine supply and locally specific logistical and vaccine delivery parameters, add complexity to national CPH strategies and amplify the urgent need for effective CPH policies. Big Data and Artificial Intelligence Machine Learning techniques and collaborations can be instrumental in an accurate, timely, locally nuanced analysis of multiple data sources to inform CPH decision-making, vaccination strategies and their staged roll-out. The Africa-Canada Artificial Intelligence and Data Innovation Consortium (ACADIC) has been established to develop and employ machine learning techniques to design CPH strategies in Africa, which requires ongoing collaboration, testing and development to maximize the equity and effectiveness of COVID-19 related CPH interventions.

Medical student researchers in Colombia and associated factors with publication: a cross-sectional study

BackgroundGaps between evidence-based research and clinical-public health practice have been evident for decades. One of the aims of medical student research is to close this gap. Accordingly, evaluating individual and environmental factors that influence participation of medical students in research are needed to understand and identify potential targets for action. This study aims to identify characteristics of medical student researchers in Colombia and the associated factors with scientific publications.MethodsA cross-sectional study of Colombian medical students involved in research using a validated, self-administered, online survey. The survey was distributed through the Colombian Association of Medical Students’ Associations (ASCEMCOL). Data sets were analyzed using descriptive and summary statistics. Bivariate analysis and a multiple logistic regression model were conducted to identify predictors of scientific publications.ResultsA total of 133 responses were analyzed from students at 12 Colombian cities and 20 higher-education institutions. Although 94% of responders had at least one research proposal, only 57% had completed a project, and 17% had published their findings. Barriers for undertaking research included time restrictions and a lack of mentorship. Motivational factors included opportunity to publish findings and good mentorship. Students planning to do a specialization (OR = 3.25; 95% Confidence interval [CI] = 1.27–8.30), innovators (OR = 3.52; 95%CI = 1.30–9.52) and committed (OR = 3.39; 95%CI = 1.02–11.29), those who had previously published their findings (OR 9.13 IC95% 2.57–32.48), and were further in their medical education (OR 2.26 IC95% 1.01–5.07), were more likely to publish scientific papers.ConclusionsOur findings describe medical students understanding of the process of conducting research in Colombia. Although there appears to be motivation to participate in research, very few students achieve publication. Barriers such as time constraints and mentorship seem to play a critical role. This highlights opportunities where barriers to research can be overcome in medical school and other levels.

The MOSART Database: Linking the SART CORS Clinical Database to the Population-Based Massachusetts PELL Reproductive Public Health Data System

Although Assisted Reproductive Technology (ART) births make up 1.6% of births in the US, the impact of ART on subsequent infant and maternal health is not well understood. Clinical ART treatment records linked to population data would be a powerful tool to study long term outcomes among those treated or not by ART. This paper describes the development of a database intended to accomplish this task. We constructed the Massachusetts Outcomes Study of Assisted Reproductive Technology (MOSART) database by linking the Society of Assisted Reproductive Technologies Clinical Outcomes Reporting System (SART CORS) and the Massachusetts (MA) Pregnancy to Early Life Longitudinal (PELL) data systems for children born to MA resident women at MA hospitals between July 2004 and December 2008. PELL data representing 282,971 individual women and their 334,152 deliveries and 342,035 total births were linked with 48,578 cycles of ART treatment in SART CORS delivered to MA residents or women receiving treatment in MA clinics, representing 18,439 eligible women of whom 9,326 had 10,138 deliveries in this time period. A deterministic five phase linkage algorithm methodology was employed. Linkage results, accuracy, and concordance analyses were examined. We linked 9,092 (89.7%) SART CORS outcome records to PELL delivery records overall, including 95.0% among known MA residents treated in MA clinics; 70.8% with full exact matches. There were minimal differences between matched and unmatched delivery records, except for unknown residency and out-of-state ART site. There was very low concordance of reported use of ART treatment between SART CORS and PELL (birth certificate) data. A total of 3.4% of MA children (11,729) were identified from ART assisted pregnancies (6,556 singletons; 5,173 multiples). The MOSART linked database provides a strong basis for further longitudinal ART outcomes studies and supports the continued development of potentially powerful linked clinical-public health databases.

Assessing Integration of Clinical and Public Health Skills in Preventive Medicine Residencies: Using Competency Mapping

To evaluate the utility of a competency mapping process for assessing the integration of clinical and public health skills in a newly developed Community Health Center (CHC) rotation at the University of Michigan School of Public Health Preventive Medicine residency. Learning objectives for the CHC rotation were derived from the Accreditation Council for Graduate Medical Education core clinical preventive medicine competencies. CHC learning objectives were mapped to clinical preventive medicine competencies specific to the specialty of public health and general preventive medicine. Objectives were also mapped to The Council on Linkages Between Academia and Public Health Practice's Tier-2 Core Competencies for Public Health Professionals. CHC learning objectives mapped to all four (100%) of the public health and general preventive medicine clinical preventive medicine competencies. CHC population-level learning objectives mapped to 32 (94%) of 34 competencies for public health professionals. Utilizing competency mapping to assess clinical-public health integration in a new CHC rotation proved to be feasible and useful. Clinical preventive medicine learning objectives for a CHC rotation can also address public health competencies.

Towards Automatic Diabetes Case Detection and ABCS Protocol Compliance Assessment

According to the American Diabetes Association, the implementation of the standards of care for diabetes has been suboptimal in most clinical settings. Diabetes is a disease that had a total estimated cost of $174 billion in 2007 for an estimated diabetes-affected population of 17.5 million in the United States. With the advent of electronic medical records (EMR), tools to analyze data residing in the EMR for healthcare surveillance can help reduce the burdens experienced today. This study was primarily designed to evaluate the efficacy of employing clinical natural language processing to analyze discharge summaries for evidence indicating a presence of diabetes, as well as to assess diabetes protocol compliance and high risk factors. Three sets of algorithms were developed to analyze discharge summaries for: (1) identification of diabetes, (2) protocol compliance, and (3) identification of high risk factors. The algorithms utilize a common natural language processing framework that extracts relevant discourse evidence from the medical text. Evidence utilized in one or more of the algorithms include assertion of the disease and associated findings in medical text, as well as numerical clinical measurements and prescribed medications. The diabetes classifier was successful at classifying reports for the presence and absence of diabetes. Evaluated against 444 discharge summaries, the classifier's performance included macro and micro F-scores of 0.9698 and 0.9865, respectively. Furthermore, the protocol compliance and high risk factor classifiers showed promising results, with most F-measures exceeding 0.9. The presented approach accurately identified diabetes in medical discharge summaries and showed promise with regards to assessment of protocol compliance and high risk factors. Utilizing free-text analytic techniques on medical text can complement clinical-public health decision support by identifying cases and high risk factors.