scholarly journals Developing Risk Assessment Criteria and Predicting High- and Low-Dengue Risk Villages for Strengthening Dengue Prevention Activities: Community Participatory Action Research, Thailand

2021 ◽  
Vol 12 ◽  
pp. 215013272110132
Author(s):  
Charuai Suwanbamrung ◽  
Cua Ngoc Le ◽  
Supreecha Kaewsawat ◽  
Nirachon Chutipattana ◽  
Patthanasak Khammaneechan ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Computer Program ◽  
Risk Prediction ◽  
Assessment Criteria ◽  
Morbidity Rate ◽  
Health Providers ◽  
Dengue Outbreak ◽  
Prevention Activities ◽  
Dengue Prevention ◽  
Prediction Criteria

Background: Risk assessment criteria for predicting dengue outbreak must be appropriated at village levels. We aimed to develop risk dengue village prediction criteria, predict village dengue risk, and strengthen dengue prevention based on community participation. Methods: This participatory research conducted in Southern Thailand included the following 5 phases: (i) preparing communities in 3 districts; (ii) developing risk dengue village prediction criteria; (iii) applying computer program; (iv) predicting village dengue risk with 75 public health providers in 39 PCUs; and (v) utilizing findings to strengthen dengue prevention activities in 220 villages. Data collecting for prediction used secondary data from primary care units in the past 5 year and current year. Descriptive statistics used calculating criteria and comparing with standard level to adjust score of risk. Results: Risk dengue village assessment criteria had 2 aspects: dengue severity (3 factors) and dengue outbreak opportunity (3 factors). Total scores were 33 points and cut-off of 17 points for high and low dengue risks villages. All criteria were applied using computer program ( http://surat.denguelim.com ). Risk prediction involved stakeholder participation in 220 villages, and used for strengthening dengue prevention activities. The concept of integrated vector management included larval indices surveillance system, garbage management, larval indices level lower than the standard, community capacity activities for dengue prevention, and school-based dengue prevention. The risk prediction criteria and process mobilized villages for dengue prevention activities to decrease morbidity rate. Conclusion: Dengue risk assessment criteria were appropriated within the village, with its smallest unit, the household, included. The data can be utilized at village levels for evaluating dengue outbreak risks.

scholarly journals Risk assessment in precapillary pulmonary hypertension: a comparative analysis

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Thomas Sonnweber ◽  
Eva-Maria Schneider ◽  
Manfred Nairz ◽  
Igor Theurl ◽  
Günter Weiss ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Pulmonary Hypertension ◽  
High Risk ◽  
Risk Stratification ◽  
Risk Prediction ◽  
Mortality Risk ◽  
Assessment Tool ◽  
Risk Models ◽  
Non Invasive

Abstract Background Risk stratification is essential to assess mortality risk and guide treatment in patients with precapillary pulmonary hypertension (PH). We herein compared the accuracy of different currently used PH risk stratification tools and evaluated the significance of particular risk parameters. Methods We conducted a retrospective longitudinal observational cohort study evaluating seven different risk assessment approaches according to the current PH guidelines. A comprehensive assessment including multi-parametric risk stratification was performed at baseline and 4 yearly follow-up time-points. Multi-step Cox hazard analysis was used to analyse and refine risk prediction. Results Various available risk models effectively predicted mortality in patients with precapillary pulmonary hypertension. Right-heart catheter parameters were not essential for risk prediction. Contrary, non-invasive follow-up re-evaluations significantly improved the accuracy of risk estimations. A lack of accuracy of various risk models was found in the intermediate- and high-risk classes. For these patients, an additional evaluation step including assessment of age and right atrium area improved risk prediction significantly. Discussion Currently used abbreviated versions of the ESC/ERS risk assessment tool, as well as the REVEAL 2.0 and REVEAL Lite 2 based risk stratification, lack accuracy to predict mortality in intermediate- and high-risk precapillary pulmonary hypertension patients. An expanded non-invasive evaluation improves mortality risk prediction in these individuals.

scholarly journals Cardiovascular disease risk prediction in older people: a qualitative study

2021 ◽  
pp. BJGP.2020.1038
Author(s):  
Denise Ann Taylor ◽  
Katharine Wallis ◽  
Sione Feki ◽  
Sione Segili Moala ◽  
Manusiu He-Naua Esther Latu ◽  
...  
Keyword(s):  
Cardiovascular Disease ◽  
Risk Assessment ◽  
Qualitative Study ◽  
Older People ◽  
Focus Groups ◽  
Risk Prediction ◽  
Heart Attack ◽  
Clinical Decision Making ◽  
Community Dwelling ◽  
Cvd Risk

Background: Despite cardiovascular disease (CVD) risk prediction equations becoming more widely available for people aged 75 years and over, views of older people on CVD risk assessment are unknown. Aim: To explore older people’s views on CVD risk prediction and its assessment. Design and Setting: Qualitative study of community dwelling older New Zealanders. Methods: We purposively recruited a diverse group of older people. Semi-structured interviews and focus groups were conducted, transcribed verbatim and thematically analysed. Results: Thirty-nine participants (mean age 74 years) of Māori, Pacific, South Asian and European ethnicities participated in one of 26 interviews or three focus groups. Three key themes emerged, (1) Poor knowledge and understanding of cardiovascular disease and its risk assessment, (2) Acceptability and perceived benefit of knowing and receiving advice on managing personal cardiovascular risk; and (3) Distinguishing between CVD outcomes; stroke and heart attack are not the same. Most participants did not understand CVD terms but were familiar with ‘heart attack,’ ‘stroke’ and understood lifestyle risk factors for these events. Participants valued CVD outcomes differently, fearing stroke and disability which might adversely affect independence and quality of life, but being less concerned about a heart attack, perceived as causing less disability and swifter death. These findings and preferences were similar across ethnic groups. Conclusion: Older people want to know their CVD risk and how to manage it, but distinguish between CVD outcomes. To inform clinical decision making for older people, risk prediction tools should provide separate event types rather than just composite outcomes.

Implementing machine learning algorithms for suicide risk prediction in clinical practice: A focus group study

2021 ◽  
Author(s):  
Kate Bentley ◽  
Kelly Zuromski ◽  
Rebecca Fortgang ◽  
Emily Madsen ◽  
Daniel Kessler ◽  
...  
Keyword(s):  
Machine Learning ◽  
Risk Assessment ◽  
Clinical Practice ◽  
Risk Prediction ◽  
Suicide Risk ◽  
Prediction Models ◽  
Clinical Decision ◽  
Machine Learning Algorithms ◽  
Suicide Risk Assessment ◽  
Focus Group Study

Background: Interest in developing machine learning algorithms that use electronic health record data to predict patients’ risk of suicidal behavior has recently proliferated. Whether and how such models might be implemented and useful in clinical practice, however, remains unknown. In order to ultimately make automated suicide risk prediction algorithms useful in practice, and thus better prevent patient suicides, it is critical to partner with key stakeholders (including the frontline providers who will be using such tools) at each stage of the implementation process.Objective: The aim of this focus group study was to inform ongoing and future efforts to deploy suicide risk prediction models in clinical practice. The specific goals were to better understand hospital providers’ current practices for assessing and managing suicide risk; determine providers’ perspectives on using automated suicide risk prediction algorithms; and identify barriers, facilitators, recommendations, and factors to consider for initiatives in this area. Methods: We conducted 10 two-hour focus groups with a total of 40 providers from psychiatry, internal medicine and primary care, emergency medicine, and obstetrics and gynecology departments within an urban academic medical center. Audio recordings of open-ended group discussions were transcribed and coded for relevant and recurrent themes by two independent study staff members. All coded text was reviewed and discrepancies resolved in consensus meetings with doctoral-level staff. Results: Though most providers reported using standardized suicide risk assessment tools in their clinical practices, existing tools were commonly described as unhelpful and providers indicated dissatisfaction with current suicide risk assessment methods. Overall, providers’ general attitudes toward the practical use of automated suicide risk prediction models and corresponding clinical decision support tools were positive. Providers were especially interested in the potential to identify high-risk patients who might be missed by traditional screening methods. Some expressed skepticism about the potential usefulness of these models in routine care; specific barriers included concerns about liability, alert fatigue, and increased demand on the healthcare system. Key facilitators included presenting specific patient-level features contributing to risk scores, emphasizing changes in risk over time, and developing systematic clinical workflows and provider trainings. Participants also recommended considering risk-prediction windows, timing of alerts, who will have access to model predictions, and variability across treatment settings.Conclusions: Providers were dissatisfied with current suicide risk assessment methods and open to the use of a machine learning-based risk prediction system to inform clinical decision-making. They also raised multiple concerns about potential barriers to the usefulness of this approach and suggested several possible facilitators. Future efforts in this area will benefit from incorporating systematic qualitative feedback from providers, patients, administrators, and payers on the use of new methods in routine care, especially given the complex, sensitive, and unfortunately still stigmatized nature of suicide risk.

The Practical Challenges of Implementing Algorithmic Regulation for Public Services

2019 ◽  
pp. 150-177
Author(s):  
Alex Griffiths
Keyword(s):  
Higher Education ◽  
Risk Assessment ◽  
Health Care ◽  
Public Sector ◽  
Case Studies ◽  
Risk Prediction ◽  
Public Services ◽  
The Public ◽  
Regulatory Domains ◽  
Limited Effectiveness

This chapter focuses on one particularly salient application of algorithmic regulation in the public sector—for the purposes of risk assessment to inform decisions about the allocation of enforcement resources, focusing on their accuracy and effectiveness in risk prediction. Drawing on two UK case studies in health care and higher education, it highlights the limited effectiveness of algorithmic regulation in these contexts, drawing attention to the pre-requisites for algorithmic regulation to fully play to its predictive strengths. In so doing, it warns against any premature application of algorithmic regulation to ever-more regulatory domains, serving as a sober reminder that delivering on the claimed promises of algorithmic regulation is anything but simple, straightforward or ‘seamless’.

Identifying Some Risk Assessment Criteria

1997 ◽  
pp. 122-154
Author(s):  
Nigel Parton ◽  
David Thorpe ◽  
Corinne Wattam
Keyword(s):  
Risk Assessment ◽  
Assessment Criteria

Identification and Referral of Families at High Risk for Cancer Susceptibility

2002 ◽  
Vol 20 (2) ◽  
pp. 528-537 ◽  
Author(s):  
Kevin M. Sweet ◽  
Terry L. Bradley ◽  
Judith A. Westman
Keyword(s):  
Risk Assessment ◽  
Family History ◽  
High Risk ◽  
Computer Program ◽  
Medical Record ◽  
Comprehensive Cancer Center ◽  
Family History Information ◽  
Cancer History ◽  
History Information ◽  
Family Cancer History

PURPOSE: Obtainment of family history and accurate assessment is essential for the identification of families at risk for hereditary cancer. Our study compared the extent to which the family cancer history in the physician medical record reflected that entered by patients directly into a touch-screen family history computer program. PATIENTS AND METHODS: The study cohort consisted of 362 patients seen at a comprehensive cancer center ambulatory clinic over a 1-year period who voluntarily used the computer program and were a mixture of new and return patients. The computer entry was assessed by genetics staff and then compared with the medical record for corroboration of family history information and appropriate physician risk assessment. RESULTS: Family history information from the medical record was available for comparison to the computer entry in 69%. It was most often completed on new patients only and not routinely updated. Of the 362 computer entries, 101 were assigned to a high-risk category. Evidence in the records confirmed 69 high-risk individuals. Documentation of physician risk assessment (ie, notation of significant family cancer history or hereditary risk) was found in only 14 of the high-risk charts. Only seven high-risk individuals (6.9%) had evidence of referral for genetic consultation. CONCLUSION: This study demonstrates the need to collect family history information on all new and established patients in order to perform adequate cancer risk assessment. The lack of identification of patients at highest risk seems to be directly correlated with insufficient data collection, risk assessment, and documentation by medical staff.

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