Fields In Health Research Articles

A Conceptual Model for Health Impact Assessment of Personalized Prevention Policies

Abstract Background Personalized prevention and pharmacogenomics are emerging fields in global health, promoting tailored approaches to disease prevention and individualized drug therapy, based on the individuals’ genetic profile. However, adoption of these technologies in public health is challenged by the possibility of health disparities. Health Impact Assessment (HIA) is a useful tool to address this issue, by examining how policies differentially impact population groups, guiding stakeholders to more equitable and inclusive outcomes. Solid frameworks for assessing health impacts are needed to avoid heterogeneity when accounting for equity outcomes in HIA. This study tested a conceptual model for HIA in personalized medicine, using a case study in pharmacogenetics to map potential impacts and outcomes, with a focus on health equity. Methods A conceptual model for a HIA was developed to map expected impacts and outcomes of a health policy, aimed at enforcing DPYD genotype-guided dosing to prevent fluoropyrimidine toxicity in colorectal cancer patients. The conceptual model was developed based on literature reviews and consultation with stakeholders. Identification of relevant impacts guided selection of indicators and quantitative impact assessment using a Markov chain model. Results Impact pathways were identified using a three-tiered reference framework encompassing individual and population perspectives, organizational factors, and overarching health system considerations. The final diagram outlined key outcomes in four categories: health, organizational, economic and equity. HIA projections using this model were obtained for the DPYD testing policy. Conclusions In the rise of personalized medicine and pharmacogenomics, health inequalities challenges need to be addressed before implementation in healthcare. This study provided a novel HIA conceptual model for personalized prevention policies based on genomic information, with an emphasis on equity. Key messages • HIA is an adequate tool to evaluate potential health inequalities resulting from personalized medicine policies in healthcare. • This study provides a validated framework for HIA of personalized prevention policies grounded in genomic data.

Isometric Force-Time Curve Assessment: Accuracy, Precision, and Repeatability of a Mobile Application and Portable and Lightweight Device.

Strength assessment is one of the main fields in sports performance, physical rehabilitation, physical activity, and health. We aimed to compare maximal voluntary isometric contractions (MVICs) and paired voluntary isometric contractions (VICs) of knee extensors between an isokinetic dynamometer (BIODEX) and a portable and lightweight device (DINABANG). From 19 volunteers (age: 28.7 ± 7 years; body mass: 72 ± 10 kg; and height: 173 ± 7 cm) we obtained 114 paired MVIC measures and, from the force-time curves of these repetitions, 22,507 paired VIC measures of knee extensors. We observed "excellent" repeatability for MVICs (ICC:1.00; p < 0.001) between BIODEX (247 ± 79.5 Nm) and DINABANG (247 ± 74.8 Nm), with "trivial" effect (mean difference: 0.12 Nm (0.02%); 95%CI: -0.13 to 0.23 Nm; p = 0.606; d = 0.048). Bland-Altman plots revealed high accuracy for MVIC (bias: 0.12 Nm) and consistent distribution (precision) inside the limits of agreement (-4.81 to 5.06 Nm) and respective 95%CI. "Excellent" repeatability was also observed for VICs (ICC:1.00; p < 0.001) between BIODEX (219 ± 84.1 Nm) and DINABANG (218 ± 84.0 Nm), with "trivial" effect (0.24 Nm (0.11%); 0.08 to 0.11 Nm; p < 0.001; d = 0.100). Bland-Altman plots revealed high accuracy for VICs (bias: 0.24 Nm) and consistent distribution (precision) inside the limits of agreement (-4.5 to 4.9 Nm) and respective 95%CI. DINABANG is accurate, precise, and reliable in torque measurement.

Extracting Critical Information from Unstructured Clinicians' Notes Data to Identify Dementia Severity Using a Rule-Based Approach: Feasibility Study.

The severity of Alzheimer disease and related dementias (ADRD) is rarely documented in structured data fields in electronic health records (EHRs). Although this information is important for clinical monitoring and decision-making, it is often undocumented or "hidden" in unstructured text fields and not readily available for clinicians to act upon. We aimed to assess the feasibility and potential bias in using keywords and rule-based matching for obtaining information about the severity of ADRD from EHR data. We used EHR data from a large academic health care system that included patients with a primary discharge diagnosis of ADRD based on ICD-9 (International Classification of Diseases, Ninth Revision) and ICD-10 (International Statistical Classification of Diseases, Tenth Revision) codes between 2014 and 2019. We first assessed the presence of ADRD severity information and then the severity of ADRD in the EHR. Clinicians' notes were used to determine the severity of ADRD based on two criteria: (1) scores from the Mini Mental State Examination and Montreal Cognitive Assessment and (2) explicit terms for ADRD severity (eg, "mild dementia" and "advanced Alzheimer disease"). We compiled a list of common ADRD symptoms, cognitive test names, and disease severity terms, refining it iteratively based on previous literature and clinical expertise. Subsequently, we used rule-based matching in Python using standard open-source data analysis libraries to identify the context in which specific words or phrases were mentioned. We estimated the prevalence of documented ADRD severity and assessed the performance of our rule-based algorithm. We included 9115 eligible patients with over 65,000 notes from the providers. Overall, 22.93% (2090/9115) of patients were documented with mild ADRD, 20.87% (1902/9115) were documented with moderate or severe ADRD, and 56.20% (5123/9115) did not have any documentation of the severity of their ADRD. For the task of determining the presence of any ADRD severity information, our algorithm achieved an accuracy of >95%, specificity of >95%, sensitivity of >90%, and an F1-score of >83%. For the specific task of identifying the actual severity of ADRD, the algorithm performed well with an accuracy of >91%, specificity of >80%, sensitivity of >88%, and F1-score of >92%. Comparing patients with mild ADRD to those with more advanced ADRD, the latter group tended to contain older, more likely female, and Black patients, and having received their diagnoses in primary care or in-hospital settings. Relative to patients with undocumented ADRD severity, those with documented ADRD severity had a similar distribution in terms of sex, race, and rural or urban residence. Our study demonstrates the feasibility of using a rule-based matching algorithm to identify ADRD severity from unstructured EHR report data. However, it is essential to acknowledge potential biases arising from differences in documentation practices across various health care systems.

Development of a 3-Step theory of suicide ontology to facilitate 3ST factor extraction from clinical progress notes

ObjectiveSuicide risk prediction algorithms at the Veterans Health Administration (VHA) do not include predictors based on the 3-Step Theory of suicide (3ST), which builds on hopelessness, psychological pain, connectedness, and capacity for suicide. These four factors are not available from structured fields in VHA electronic health records, but they are found in unstructured clinical text. An ontology and controlled vocabulary that maps psychosocial and behavioral terms to these factors does not exist. The objectives of this study were 1) to develop an ontology with a controlled vocabulary of terms that map onto classes that represent the 3ST factors as identified within electronic clinical progress notes, and 2) to determine the accuracy of automated extractions based on terms in the controlled vocabulary. MethodsA team of four annotators did linguistic annotation of 30,000 clinical progress notes from 231 Veterans in VHA electronic health records who attempted suicide or who died by suicide for terms relating to the 3ST factors. Annotation involved manually assigning a label to words or phrases that indicated presence or absence of the factor (polarity). These words and phrases were entered into a controlled vocabulary that was then used by our computational system to tag 14 million clinical progress notes from Veterans who attempted or died by suicide after 2013. Tagged text was extracted and machine-labelled for presence or absence of the 3ST factors. Accuracy of these machine-labels was determined for 1000 randomly selected extractions for each factor against a ground truth created by our annotators. ResultsLinguistic annotation identified 8486 terms that related to 33 subclasses across the four factors and polarities. Precision of machine-labeled extractions ranged from 0.73 to 1.00 for most factor-polarity combinations, whereas recall was somewhat lower 0.65–0.91. ConclusionThe ontology that was developed consists of classes that represent each of the four 3ST factors, subclasses, relationships, and terms that map onto those classes which are stored in a controlled vocabulary (https://bioportal.bioontology.org/ontologies/THREE-ST). The use case that we present shows how scores based on clinical notes tagged for terms in the controlled vocabulary capture meaningful change in the 3ST factors during weeks preceding a suicidal event.

PP42 Insights Of Health Technology Assessment In Brazilian Health Unified System: Areas Of Interests In Health

IntroductionThe National Committee for Health Technology Incorporation of the Brazilian Public Health System’ s (Conitec) principle is to advise the Ministry of Health (MS) in the tasks related to incorporation, exclusion or modification of any health technologies into the Unified Health System (SUS). Moreover, this also involves alteration of clinical protocols or therapeutic guidelines. All of the recommendations consider the international classification of diseases (CID) as a common language that allows health professionals and managers to understand standardized information, to identify trends and benefits of recommendations in each therapeutic area.MethodsThis exploratory, descriptive and retrospective study aims to provide qualitative and quantitative data from the technologies evaluated by the Conitec in the period June 2012 to November 2022. Data were extracted in Conitec’s website.ResultsThe searches resulted in 763 recommendations in total. Among them, the most evaluated therapeutic area was Infectology with 126 technologies (16.5%). In this field the highlighted diseases and conditions were Hepatitis 42 (33.3%); HIV 23 (18.3%) and COVID-19 11 (8.7%). In Oncology, 113 recommended technologies (14.8%) were identified, in order of prominence for the diseases: Breast Cancer 21 (18.6%); Colorectal Cancer 11 (9.7%); Leukemias 17 (15.0%). In the Respiratory Diseases area, 89 technologies (11.7%) were recommended, among them: Chronic obstructive pulmonary disease (COPD) 17 (19.1%); Asthma 15 (16.9%) and COVID-19 11 (12.4%). These results clarify which diseases are most needing new technologies to be treated.ConclusionsThe results show what conditions and fields in health needs to be prioritized for public policies and prevention measures. This study demonstrates how important is to make accessible the public health information, improving public knowledge and social actions in SUS.

Interventions to increase vaccination against COVID-19, influenza and pertussis during pregnancy: a systematic review and meta-analysis.

Pregnant women and their babies face significant risks from three vaccine-preventable diseases: COVID-19, influenza and pertussis. However, despite these vaccines' proven safety and effectiveness, uptake during pregnancy remains low. We conducted a systematic review (PROSPERO CRD42023399488; January 2012-December 2022 following PRISMA guidelines) of interventions to increase COVID-19/influenza/pertussis vaccination in pregnancy. We searched nine databases, including grey literature. Two independent investigators extracted data; discrepancies were resolved by consensus. Meta-analyses were conducted using random-effects models to estimate pooled effect sizes. Heterogeneity was assessed using the I2 statistics. From 2681 articles, we identified 39 relevant studies (n = 168 262 participants) across nine countries. Fifteen studies (39%) were randomized controlled trials (RCTs); the remainder were observational cohort, quality-improvement or cross-sectional studies. The quality of 18% (7/39) was strong. Pooled results of interventions to increase influenza vaccine uptake (18 effect estimates from 12 RCTs) showed the interventions were effective but had a small effect (risk ratio = 1.07, 95% CI 1.03, 1.13). However, pooled results of interventions to increase pertussis vaccine uptake (10 effect estimates from six RCTs) showed no clear benefit (risk ratio = 0.98, 95% CI 0.94, 1.03). There were no relevant RCTs for COVID-19. Interventions addressed the 'three Ps': patient-, provider- and policy-level strategies. At the patient level, clear recommendations from healthcare professionals backed by text reminders/written information were strongly associated with increased vaccine uptake, especially tailored face-to-face interventions, which addressed women's concerns, dispelled myths and highlighted benefits. Provider-level interventions included educating healthcare professionals about vaccines' safety and effectiveness and reminders to offer vaccinations routinely. Policy-level interventions included financial incentives, mandatory vaccination data fields in electronic health records and ensuring easy availability of vaccinations. Interventions had a small effect on increasing influenza vaccination. Training healthcare providers to promote vaccinations during pregnancy is crucial and could be enhanced by utilizing mobile health technologies.

Using a Rapid Knowledge Translation Approach for Better Sexual and Reproductive Health and Rights in Bangladesh, Burundi, Indonesia, and Jordan.

Translation of knowledge into policy and practice is important to prevent sexual reproductive health and rights (SRHR)-related morbidity and mortality and ensure access to rights. Existing approaches to knowledge translation are often relatively rigid and implicitly assume linear processes, leading to time-consuming processes that are not tailored to countries' needs. SRHR knowledge platforms designed and implemented a collaborative rapid improvement model for knowledge translation (CRIM-KT) in Burundi, Bangladesh, Indonesia, and Jordan. The approach consisted of learning sessions and action periods aimed at improving policies and practices addressing the prevention of child marriage and teenage pregnancies. To evaluate the approach, a participatory action learning process took place throughout the implementation (September 2017 and January 2019). An end evaluation was conducted using a desk review of project documentation, in-depth interviews, and a focus group discussion to document the process, outcomes, and lessons learned. In Indonesia, a local government policy was changed that aims to prevent child marriage by avoiding misinterpretation of a local cultural practice. In Jordan, the cabinet endorsed a national action plan to prevent child marriage and changes in practice took place. In Burundi, no tangible changes in policy and practice in SRHR were observed. In Bangladesh, practice changed by strengthening coordination for collaboration and exchange among stakeholders to prevent child marriage. In all countries, the approach considerably strengthened participants' knowledge translation capacities. The CRIM-KT led to improvements in policy and practice in a relatively short time frame and different contexts. This can be explained by the systematic, structured, and participatory approach, allowing for contextual adaptation and involvement of stakeholders, as well as the cross-learning on 2 levels (international and country collaboration teams). The principles of the CRIM-KT may be further developed and applied in other fields in global health to strengthen knowledge translation processes.

Descriptive Study of Safety and Health Training Content in Iranian Universities and Higher Education Institutes

The root cause of many workplace incidents is the lack of knowledge on occupational safety and health principles. Despite the safety and health authorities in organizations, safety and health is the general responsibility of all other experts working in an organization. Experts and managers are expected to think, decide and work safely in workplaces. However, we don’t have enough information about the level of safety and health-related education when they were in universities. In addition, there is not too much reliable published reports on the academic teaching of safety and health courses and syllabus in higher education in Iran. Therefore, the current study was aimed to provide a general view of the status of occupational safety and health teaching subjects in different academic disciplines (excluding specialized fields in occupational safety and health) in the Iranian higher educational system. Firstly, the latest edition of the entire program (curricula) of all academic disciplines held in Iranian universities was downloaded. Secondly, the required information, including discipline title, program’s degree, number of required (core) and elective (non-core) courses, and program’s date of approval, were recorded in an Excel sheet. Every course’s title and syllabus were then studied to find out any safety and health-related topics. Finally, data were analyzed and reported. 251 academic disciplines in the Ministry of Health and Medical Education (MOHME) and 771 disciplines in the Ministry of Science, Research and Technology (MSRT) were investigated. Most of the curriculums have been revised by both ministries since 2011. Results showed that 20 percent of the MOHME‎ ‎disciplines and 10.4 percent of MSRT‎ disciplines were included in the occupational safety and health courses. The number of courses with health and safety content (syllabus outline) in the MOHME ‎‎and the MSRT‎ were 168 and 173. Most health and safety courses presented in the MOHME academic majors were in the basic medical sciences category of disciplines. A majority of MSRT covered academic safety and health courses and topics were presented in the engineering category of disciplines. Presenting lessons with health and safety content at the MOHME ‎was more practical than that of the MSRT. The quality and quantity of teaching on safety and health topic were not at satisfactory levels. Revising the course syllabus of the academic disciplines and adding theoretical and practical courses related to each field's specific health and safety curriculum can make a significant contribution in improving the general knowledge of the safety and health of the university graduates. In turn, it improves future workplace safety and health conditions, managers’ insights and decision-makers on the importance of safety and health at work. It protects the people's life and the property of the organization.

Text mining occupations from the mental health electronic health record: a natural language processing approach using records from the Clinical Record Interactive Search (CRIS) platform in south London, UK

ObjectivesWe set out to develop, evaluate and implement a novel application using natural language processing to text mine occupations from the free-text of psychiatric clinical notes.DesignDevelopment and validation of a...

Machine Learning Electronic Health Record Identification of Patients with Rheumatoid Arthritis: Algorithm Pipeline Development and Validation Study.

BackgroundFinancial codes are often used to extract diagnoses from electronic health records. This approach is prone to false positives. Alternatively, queries are constructed, but these are highly center and language specific. A tantalizing alternative is the automatic identification of patients by employing machine learning on format-free text entries.ObjectiveThe aim of this study was to develop an easily implementable workflow that builds a machine learning algorithm capable of accurately identifying patients with rheumatoid arthritis from format-free text fields in electronic health records.MethodsTwo electronic health record data sets were employed: Leiden (n=3000) and Erlangen (n=4771). Using a portion of the Leiden data (n=2000), we compared 6 different machine learning methods and a naïve word-matching algorithm using 10-fold cross-validation. Performances were compared using the area under the receiver operating characteristic curve (AUROC) and the area under the precision recall curve (AUPRC), and F1 score was used as the primary criterion for selecting the best method to build a classifying algorithm. We selected the optimal threshold of positive predictive value for case identification based on the output of the best method in the training data. This validation workflow was subsequently applied to a portion of the Erlangen data (n=4293). For testing, the best performing methods were applied to remaining data (Leiden n=1000; Erlangen n=478) for an unbiased evaluation.ResultsFor the Leiden data set, the word-matching algorithm demonstrated mixed performance (AUROC 0.90; AUPRC 0.33; F1 score 0.55), and 4 methods significantly outperformed word-matching, with support vector machines performing best (AUROC 0.98; AUPRC 0.88; F1 score 0.83). Applying this support vector machine classifier to the test data resulted in a similarly high performance (F1 score 0.81; positive predictive value [PPV] 0.94), and with this method, we could identify 2873 patients with rheumatoid arthritis in less than 7 seconds out of the complete collection of 23,300 patients in the Leiden electronic health record system. For the Erlangen data set, gradient boosting performed best (AUROC 0.94; AUPRC 0.85; F1 score 0.82) in the training set, and applied to the test data, resulted once again in good results (F1 score 0.67; PPV 0.97).ConclusionsWe demonstrate that machine learning methods can extract the records of patients with rheumatoid arthritis from electronic health record data with high precision, allowing research on very large populations for limited costs. Our approach is language and center independent and could be applied to any type of diagnosis. We have developed our pipeline into a universally applicable and easy-to-implement workflow to equip centers with their own high-performing algorithm. This allows the creation of observational studies of unprecedented size covering different countries for low cost from already available data in electronic health record systems.

Association of Peer Comparison Emails With Electronic Health Record Documentation of Cancer Stage by Oncologists

Systematically capturing cancer stage is essential for any serious effort by health systems to monitor outcomes and quality of care in oncology. However, oncologists do not routinely record cancer stage in machine-readable structured fields in electronic health records (EHRs). To evaluate whether a peer comparison email intervention that communicates an oncologist's performance on documenting cancer stage relative to that of peer physicians was associated with increased likelihood that stage was documented in the EHR. This 12-month, randomized quality improvement pilot study aimed to increase oncologist staging documentation in the EHR. The pilot study was performed at Massachusetts General Hospital Cancer Center from October 1, 2018, to September 30, 2019. Participants included 56 oncologists across 3 practice sites who treated patients in the ambulatory setting and focused on diseases that use standardized staging systems. Data were analyzed from July 2, 2019, to March 5, 2020. Peer comparison intervention with as many as 3 emails to oncologists during 6 months that displayed the oncologist's staging documentation rate relative to all oncologists in the study sample. The primary outcome was patient-level documentation of cancer stage, defined as the likelihood that a patient's stage of disease was documented in the EHR after the patient's first (eg, index) ambulatory visit during the pilot period. Among the 56 oncologists participating (32 men [57%]), receipt of emails with peer comparison data was associated with increased likelihood of documentation of cancer stage using the structured field in the EHR (23.2% vs 13.0% of patient index visits). In adjusted analyses, this difference represented an increase of 9.0 (95% CI, 4.4-13.5) percentage points (P = .002) in the probability that a patient's cancer stage was documented, a relative increase of 69% compared with oncologists who did not receive peer comparison emails. The association increased with each email that was sent, ranging from a nonsignificant 4.0 (95% CI, -0.8 to 8.8) percentage points (P = .09) after the first email to a statistically significant 11.2 (95% CI, 4.9-17.4) percentage points (P = .003) after the third email . The association was concentrated among an oncologist's new patients (increase of 11.8 [95% CI, 6.2-17.4] percentage points; P = .001) compared with established patients (increase of 1.6 [95% CI, -2.9 to 6.1] percentage points; P = .44) and persisted for 7 months after the email communications stopped. In a quality improvement pilot trial, peer comparison emails were associated with a substantial increase in oncologist use of the structured field in the EHR to document stage of disease.

Structure-based Methods for Binding Mode and Binding Affinity Prediction for Peptide-MHC Complexes.

Understanding the mechanisms involved in the activation of an immune response is essential to many fields in human health, including vaccine development and personalized cancer immunotherapy. A central step in the activation of the adaptive immune response is the recognition, by T-cell lymphocytes, of peptides displayed by a special type of receptor known as Major Histocompatibility Complex (MHC). Considering the key role of MHC receptors in T-cell activation, the computational prediction of peptide binding to MHC has been an important goal for many immunological applications. Sequence- based methods have become the gold standard for peptide-MHC binding affinity prediction, but structure-based methods are expected to provide more general predictions (i.e., predictions applicable to all types of MHC receptors). In addition, structural modeling of peptide-MHC complexes has the potential to uncover yet unknown drivers of T-cell activation, thus allowing for the development of better and safer therapies. In this review, we discuss the use of computational methods for the structural modeling of peptide-MHC complexes (i.e., binding mode prediction) and for the structure-based prediction of binding affinity.

Assessment and Stratification of High-Impact Data Elements in Electronic Clinical Quality Measures: A Joint Data Quality Initiative Between CancerLinQ® and Cancer Treatment Centers of America.

A joint data quality initiative between the Cancer Treatment Centers of America and the ASCO big data health technology platform CancerLinQ® was initiated to document and codify the steps taken to evaluate, stratify, and determine the potential effect of data elements used for electronic clinical quality measures as captured within structured fields in electronic health records. The processes involved the identification of clinical concepts required in measure population criteria and then to map these to the corresponding components of the CancerLinQ data model. A quantitative assessment of mappings between electronic clinical quality measure clinical concepts and attributes from the CancerLinQ clinical database was performed. In parallel, a qualitative analysis of high-impact data elements from the Cancer Treatment Centers of America clinical measures was made using local, expert consensus. An impact assessment was derived using a count of the data elements across measures and the specific population criteria affected. A list of putative high-impact data elements can provide guidance for clinicians to facilitate specific data element capture related to quality metrics in an electronic environment.

Communicating Environmental Health Risks with Indigenous Populations: A Systematic Literature Review of Current Research and Recommendations for Future Studies

ABSTRACTIndigenous populations are recognized as a group who are potentially vulnerable to environmental health hazards due to their intimate relationship with and reliance on local environments for aspects of culture, health, and well-being. In many circumstances barriers to effective communication and health risk management are linked to cultural, economic, and geographic factors. A systematic literature review was conducted to consolidate peer-reviewed research on the communication of environmental health risks with Indigenous populations. The comprehensive literature review procedures included searching databases and key journals that represented various fields in communication, environmental health, and Indigenous studies. The review yielded a total of 4,469 potential articles and a total of 13 of these manuscripts met the inclusion criteria. The 13 articles were analyzed to identify lessons learned for effective risk communication. Factors that influence successful risk communication strategies with Indigenous populations on this topic include: (1) developing messages that are congruent with the populations’ cultural beliefs and understanding of the environment; (2) including Indigenous populations in message design and delivery; (3) using credible and trustworthy spokespeople in message delivery; (4) identifying and utilizing effective communication materials and channels; and (5) ensuring that messages are understandable to the target audience. Gaps in the literature include the lack of longitudinal studies that empirically measure changes in perception, awareness and behavior, as well as a general lack of theory-based research. Results from this review provide directions for future work to help guide the development of more effective health risk communication research and strategies with Indigenous populations.

Monitoring Lung Cancer Screening Use and Outcomes at Four Cancer Research Network Sites.

Lung cancer screening registries can monitor screening outcomes and improve quality of care. To describe nascent lung cancer screening programs and share efficient data collection approaches for mandatory registry reporting in four integrated health care systems of the National Cancer Institute-funded Cancer Research Network. We documented the distinctive characteristics of lung cancer screening programs, and we provide examples of strategies to facilitate data collection and describe early challenges and possible solutions. In addition, we report preliminary data on use and outcomes of screening with low-dose computed tomography at each of the participating sites. Programs varied in approaches to confirming patient eligibility, ordering screening low-dose computed tomographic scans, and coordinating follow-up care. Most data elements were collected from structured fields in electronic health records, but sites also made use of standardized order templates, local procedure codes, identifiable hashtags in radiology reports, and natural language processing algorithms. Common challenges included incomplete documentation of tobacco smoking history, difficulty distinguishing between scans performed for screening versus diagnosis or surveillance, and variable adherence with use of standardized templates. Adherence with eligibility criteria as well as the accuracy and completeness of data collection appeared to depend at least partly on availability of personnel and other resources to support the successful implementation of screening. To maximize the effectiveness of lung cancer screening, minimize the burden of data collection, and facilitate research and quality improvement, clinical workflow and information technology should be purposefully designed to ensure that patients meet eligibility criteria and receive appropriate follow-up testing.

Assessing Awareness of and Interests in Public Health Among Undergraduate Students Without Declared Majors

There are underrepresented fields in both undergraduate-level (UG) and graduate-level public health (PH) professional preparation in the United States. Demand for employment and a diversified work force in PH is growing. To understand how well UG students are aware of and knowledgeable about PH, a survey was conducted at a large urban statewide public university in the Northeastern United States. Students solicited to participate in the anonymous online survey conducted in PsychData were first- and second-year undeclared major UG students during March to April 2015 (spring semester). Of 138 consenting respondents, there were 101 validated, complete sets of responses. Variables included student course enrollment, whether parental occupation influences student choice of majors, and perceived quality of available curricula. More UG students were enrolled in introductory statistics among the PH-related courses asked about; those courses fulfilled multiple UG major requirements and could play greater roles in student awareness of PH, that is, opportunities via PH faculty/staff/guest lectures. Implications of findings include providing students hands-on experiences late in high school and early UG years, and advisors mentioning PH to students interested in basic sciences.

Concept mapping as a method to enhance evidence-based public health

In this paper we explore the suitability of concept mapping as a method for integrating knowledge from science, practice, and policy. In earlier research we described and analysed five cases of concept mapping procedures in the Netherlands, serving different purposes and fields in public health. In the current paper, seven new concept mapping studies of co-produced work are added to extend this analysis.For each of these twelve studies we analysed: (1) how the method was able to integrate knowledge from practice with scientific knowledge by facilitating dialogue and collaboration between different stakeholders in the field of public health, such as academic researchers, practitioners, policy-makers and the public; (2) how the method was able to bring theory development a step further (scientific relevance); and (3) how the method was able to act as a sound basis for practical decision-making (practical relevance).Based on the answers to these research questions, all but one study was considered useful for building more evidence-based public health, even though the extent to which they underpinned actual decision-making varied. The chance of actually being implemented in practice seems strongly related to the extent to which the responsible decision-makers are involved in the way the concept map is prepared and executed.

On Race and Medicine: Insider Perspectives ed. by Richard Garcia

Reviewed by: On Race and Medicine: Insider Perspectives ed. by Richard Garcia David Colón-Cabrera ON RACE AND MEDICINE: Insider Perspectives. Edited by Richard Garcia. Lanham, MD: Rowman & Littlefield. 2015. The fields of anthropology and sociology, in addition to health sciences, have problematized the topic of race and medicine extensively. The dubious history of medical practice towards non-white bodies has left deep impacts on the manner in which biomedicine still speaks, treats, and cares for individuals who are not white. Medicine has its own white privilege problem in the way it often sets whiteness (and maleness) as the default body to research, treat, and care for. On Race and Medicine reflects on these challenges by providing an insight into the experiences of practitioners and researchers at the intersection of race and healthcare. The book falls within the purview of current research and theory exploring the cultural, social, and political aspects of science. While the book does not specifically identify its aim and scope within Science and Technology Studies, it focuses on those involved in the production and practice of medicine. On Race and Medicine relies on narratives that characterize the multidisciplinary nature of medicine from the perspective of a diverse group of academics and health practitioners—though only a third are women. The book presents the experiences and trajectories of the collaborators and their induction to the topic of race within healthcare. Edited by Richard Garcia, the book’s four sections attempt to retrospectively challenge the manner in which health disparities have been evaluated in recent decades. The first section, Health Disparities, sets the tone by arguing how historical and environmental factors can help explain current health disparities. The Personal Essay presents the omnipresent effect that a racial and ethnic identity has in developing attitudes and behaviors towards healthcare. In Race and Medicine several [End Page 163] collaborators reflect on their own biases, attitudes, privileges, and experiences at the intersection of race and medicine. Collaborators recount their challenging experiences encountering medicine while being an ethnic/racial other or being exposed to the ethnic/racial other. Finally, in Towards Solutions, the collaborators discuss the limitations that they deal with in their work and practice. The latter sections are the core of the book since they answer the editor’s central question: “But is this form—rather than the traditional writing of social science or public health—useful, or even necessary?” (31). The use of “forensic chapters” (4) by the collaborators exemplify the manner in which medicine deals with the lived experiences of ethnic and racial minorities, and invite the reader to reflect on those challenges. Garcia and collaborators seem to be writing for health professionals who are reticent to appreciate the value of personal essays as a narrative tool to explain the complexity of race and healthcare. The editor makes a compelling, though limited, argument supporting the study of health disparities in the US. On Race and Medicine relies on an abundance of sociological and anthropological knowledge, but the editor’s discussions referencing these disciplines could have benefitted from more depth; for example, on pages 4–5 Garcia states: “I imagine the topic of health disparities as a section in a syllabus of an American studies course, along with the other sections that consider race in America.” He appears to overlook the fact that fields in anthropology, sociology, the humanities and public health have crafted entire programs and courses that examine race and medicine in a holistic manner. Similarly, Garcia’s exhortation, “I’d call for a moratorium on disparities studies if anyone were listening. We know. They exist. Enough studies already. Now let’s fix them” (160) misses the point by inadvertently minimizing the scholarship of the aforementioned disciplines. Garcia and collaborators provide contrasting and dynamic insights that challenge some of the notions of race and healthcare in a very personal way. The value of this book lies in the personal contributions alluding to the diversity of socioeconomics and relative privilege within ethnic and racial communities, and their influence on health-seeking behaviors and attitudes. At the end of the book, in regard to the challenges that the interaction of race and healthcare cause, Garcia poses the question...

Framework construction and application for global health information platform

In order to understand the global health related information rapidly and timely, a global health information platform based on big data from internet is constructed. In this paper, we introduce the framework and application of the global health information platform. The functions of information platform mainly include two aspects: to analyze and disseminate theoretical and technological progress of the main research fields in global health and to monitor real-time emergencies, epidemic situation and hot topics related to the field of global health. The objective of this research is to establish a Chinese-English bilingual information platform in the global health field. The platform would help academic researchers understand and master global health progress in time; acquire information of global health comprehensively, accurately and dynamically; and eventually help deal with the risk of global diseases, formulate appropriate health policies and actively participate in the international global health affairs.

Concept mapping as a promising method to bring practice into science

ObjectiveConcept mapping is a method for developing a conceptual framework of a complex topic for use as a guide to evaluation or planning. In concept mapping, thoughts and ideas are represented in the form of a picture or map, the content of which is determined by a group of stakeholders. This study aimed to explore the suitability of this method as a tool to integrate practical knowledge with scientific knowledge in order to improve theory development as a sound basis for practical decision-making. MethodFollowing a short introduction to the method of concept mapping, five Dutch studies, serving different purposes and fields in public health, will be described. The aim of these studies was: to construct a theoretical framework for good regional public health reporting; to design an implementation strategy for a guideline for integral local health policy; to guide the evaluation of a local integral approach of overweight and obesity in youth; to guide the construction of a questionnaire to measure the quality of postdisaster psychosocial care; and to conceptualize an integral base for formulation of ambitions and targets for the new youth healthcare programme of a regional health service. ResultsThe studies showed that concept mapping is a way to integrate practical and scientific knowledge with careful selection of participants that represent the different perspectives. Theory development can be improved through concept mapping; not by formulating new theories, but by highlighting the key issues and defining perceived relationships between topics. In four of the five studies, the resulting concept map was received as a sound basis for practical decision-making. ConclusionsConcept mapping is a valuable method for evidence-based public health policy, and a powerful instrument for facilitating dialogue, coherence and collaboration between researchers, practitioners, policy makers and the public. Development of public health theory was realized by a step-by-step approach, considering both scientific and practical knowledge. However, the external validity of the concept maps in place and time is of importance.