scholarly journals Suicide disparities across metropolitan areas in the US: A comparative assessment of socio-environmental factors using a data-driven predictive approach

PLoS ONE ◽  
2021 ◽  
Vol 16 (11) ◽  
pp. e0258824
Author(s):  
Sayanti Mukherjee ◽  
Zhiyuan Wei
Keyword(s):  
Environmental Factors ◽  
Goodness Of Fit ◽  
Metropolitan Areas ◽  
Predictive Accuracy ◽  
Climatic Factors ◽  
Data Driven ◽  
Us States ◽  
Suicide Rates ◽  
Significant Difference ◽  
The Us

Disparity in suicide rates across various metropolitan areas in the US is growing. Besides personal genomics and pre-existing mental health conditions affecting individual-level suicidal behaviors, contextual factors are also instrumental in determining region-/community-level suicide risk. However, there is a lack of quantitative approach to model the complex associations and interplays of the socio-environmental factors with the regional suicide rates. In this paper, we propose a holistic data-driven framework to model the associations of socio-environmental factors (demographic, socio-economic, and climate) with the suicide rates, and compare the key socio-environmental determinants of suicides across the large and medium/small metros of the vulnerable US states, leveraging a suite of advanced statistical learning algorithms. We found that random forest outperforms all the other models in terms of both in-sample goodness-of-fit and out-of-sample predictive accuracy, which is then used for statistical inferencing. Overall, our findings show that there is a significant difference in the relationships of socio-environmental factors with the suicide rates across the large and medium/small metropolitan areas of the vulnerable US states. Particularly, suicides in medium/small metros are more sensitive to socio-economic and demographic factors, while that in large metros are more sensitive to climatic factors. Our results also indicate that non-Hispanics, native Hawaiian or Pacific islanders, and adolescents aged 15-29 years, residing in the large metropolitan areas, are more vulnerable to suicides compared to those living in the medium/small metropolitan areas. We also observe that higher temperatures are positively associated with higher suicide rates, with large metros being more sensitive to such association compared to that of the medium/small metros. Our proposed data-driven framework underscores the future opportunities of using big data analytics in analyzing the complex associations of socio-environmental factors and inform policy actions accordingly.

scholarly journals Health-Behaviors Associated With the Growing Risk of Adolescent Suicide Attempts: A Data-Driven Cross-Sectional Study

2020 ◽  
pp. 089011712097737
Author(s):  
Zhiyuan Wei ◽  
Sayanti Mukherjee
Keyword(s):  
Health Behaviors ◽  
Goodness Of Fit ◽  
Adolescent Suicide ◽  
Suicide Attempts ◽  
Predictive Accuracy ◽  
Warning Signs ◽  
Machine Learning Algorithms ◽  
Data Driven ◽  
Cross Sectional ◽  
Increased Risk

Purpose: Identify and examine the associations between health behaviors and increased risk of adolescent suicide attempts, while controlling for socio-economic and demographic differences. Design: A data-driven analysis using cross-sectional data. Setting: Communities in the state of Montana from 1999 to 2017. Selected Montana as it persistently ranks among the top 3 vulnerable states in the U.S. over the past years. Subjects: Selected 22,447 adolescents of whom 1,631 adolescents attempted suicide at least once. Measures: Overall 29 variables (predictors) accounting for psychological behaviors, illegal substances consumption, daily activities at schools and demographic backgrounds were considered. Analysis: A library of machine learning algorithms along with the traditionally-used logistic regression were used to model and predict suicide attempt risk. Model performances—goodness-of-fit and predictive accuracy—were measured using accuracy, precision, recall and F-score metrics. Additionally, χ2 analysis was used to evaluate the statistical significance of each variable. Results: The non-parametric Bayesian tree ensemble model outperformed all other models, with 80.0% accuracy in goodness-of-fit (F-score: 0.802) and 78.2% in predictive accuracy (F-score: 0.785). Key health-behaviors identified include: being sad/hopeless ( p < 0.0001), followed by safety concerns at school ( p < 0.0001), physical fighting ( p < 0.0001), inhalant usage ( p < 0.0001), illegal drugs consumption at school ( p < 0.0001), current cigarette usage ( p < 0.0001), and having first sex at an early age (below 15 years of age). Additionally, the minority groups (American Indian/Alaska Natives, Hispanics/Latinos) ( p < 0.0001), and females ( p < 0.0001) are also found to be highly vulnerable to attempting suicides. Conclusion: Significant contribution of this work is understanding the key health-behaviors and health disparities that lead to higher frequency of suicide attempts among adolescents, while accounting for the non-linearity and complex interactions among the outcome and the exposure variables. Findings provide insights on key health-behaviors that can be viewed as early warning signs/precursors of suicide attempts among adolescents.

scholarly journals Modeling the potential climate change- induced impacts on future genus Rhipicephalus (Acari: Ixodidae) tick distribution in semi-arid areas of Raya Azebo district, Northern Ethiopia

2019 ◽  
Vol 43 (1) ◽  
Author(s):  
Meseret Hadgu ◽  
Habtamu Taddele Menghistu ◽  
Atkilt Girma ◽  
Haftu Abrha ◽  
Haftom Hagos
Keyword(s):  
Climate Change ◽  
Potential Distribution ◽  
Goodness Of Fit ◽  
Predictive Accuracy ◽  
Climatic Factors ◽  
Model Performance ◽  
Northern Ethiopia ◽  
Distribution Maps ◽  
Tick Distribution ◽  
The Future

Abstract Background Climate change is believed to be continuously affecting ticks by influencing their habitat suitability. However, we attempted to model the climate change-induced impacts on future genus Rhipicephalus distribution considering the major environmental factors that would influence the tick. Therefore, 50 tick occuance points were taken to model the potential distribution using maximum entropy (MaxEnt) software and 19 climatic variables, taking into account the ability for future climatic change under representative concentration pathways (RCPs) 4.5 and 8.5, were used. Results MaxEnt model performance was tested and found with the AUC value of 0.99 which indicates excellent goodness-of-fit and predictive accuracy. Current models predict increased temperatures, both in the mid and end terms together with possible changes of other climatic factors like precipitation which may lead to higher tick-borne disease risks associated with expansion of the range of the targeted tick distribution. Distribution maps were constructed for the current, 2050, and 2070 for the two greenhouse gas scenarios and the most dramatic scenario; RCP 8.5 produced the highest increase probable distribution range. Conclusions The future potential distribution of the genus Rhipicephalus show potential expansion to the new areas due to the future climatic suitability increase. These results indicate that the genus population of the targeted tick could emerge in areas in which they are currently lacking; increased incidence of tick-borne diseases poses further risk which can affect cattle production and productivity, thereby affecting the livelihood of smallholding farmers. Therefore, it is recommended to implement climate change adaptation practices to minimize the impacts.

scholarly journals Implications of delayed reopening in controlling the COVID-19 surge in Southern and West-Central USA

2020 ◽  
Author(s):  
Raj Dandekar ◽  
Emma Wang ◽  
George Barbastathis ◽  
Chris Rackauckas
Keyword(s):  
Data Driven ◽  
Us States ◽  
Infected Case ◽  
West Central ◽  
Case Count ◽  
Proposed Model ◽  
The Us ◽  
June July ◽  
Central Usa

1SUMMARYIn the wake of the rapid surge in the Covid-19 infected cases seen in Southern and West-Central USA in the period of June-July 2020, there is an urgent need to develop robust, data-driven models to quantify the effect which early reopening had on the infected case count increase. In particular, it is imperative to address the question: How many infected cases could have been prevented, had the worst affected states not reopened early? To address this question, we have developed a novel Covid-19 model by augmenting the classical SIR epidemiological model with a neural network module. The model decomposes the contribution of quarantine strength to the infection timeseries, allowing us to quantify the role of quarantine control and the associated reopening policies in the US states which showed a major surge in infections. We show that the upsurge in the infected cases seen in these states is strongly co-related with a drop in the quarantine/lockdown strength diagnosed by our model. Further, our results demonstrate that in the event of a stricter lockdown without early reopening, the number of active infected cases recorded on 14 July could have been reduced by more than 40% in all states considered, with the actual number of infections reduced being more than 100, 000 for the states of Florida and Texas. As we continue our fight against Covid-19, our proposed model can be used as a valuable asset to simulate the effect of several reopening strategies on the infected count evolution; for any region under consideration.

scholarly journals Implications of Delayed Reopening in Controlling the COVID-19 Surge in Southern and West-Central USA

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Raj Dandekar ◽  
Emma Wang ◽  
George Barbastathis ◽  
Chris Rackauckas
Keyword(s):  
Data Driven ◽  
Us States ◽  
Infected Case ◽  
West Central ◽  
Case Count ◽  
Proposed Model ◽  
The Us ◽  
June July ◽  
Central Usa

In the wake of the rapid surge in the COVID-19-infected cases seen in Southern and West-Central USA in the period of June-July 2020, there is an urgent need to develop robust, data-driven models to quantify the effect which early reopening had on the infected case count increase. In particular, it is imperative to address the question: How many infected cases could have been prevented, had the worst affected states not reopened early? To address this question, we have developed a novel COVID-19 model by augmenting the classical SIR epidemiological model with a neural network module. The model decomposes the contribution of quarantine strength to the infection time series, allowing us to quantify the role of quarantine control and the associated reopening policies in the US states which showed a major surge in infections. We show that the upsurge in the infected cases seen in these states is strongly corelated with a drop in the quarantine/lockdown strength diagnosed by our model. Further, our results demonstrate that in the event of a stricter lockdown without early reopening, the number of active infected cases recorded on 14 July could have been reduced by more than 40% in all states considered, with the actual number of infections reduced being more than 100,000 for the states of Florida and Texas. As we continue our fight against COVID-19, our proposed model can be used as a valuable asset to simulate the effect of several reopening strategies on the infected count evolution, for any region under consideration.

scholarly journals Modeling the COVID-19 outbreak in the United States

2020 ◽  
Author(s):  
Charit Samyak Narayanan
Keyword(s):  
Logistic Model ◽  
Goodness Of Fit ◽  
Case Fatality ◽  
The United States ◽  
Us States ◽  
Mainstream Media ◽  
Effective Interventions ◽  
The Us ◽  
Two Factors ◽  
The Impact

AbstractThe COVID-19 contagion has developed at an alarming rate in the US and as of April 24, 2020, tens of thousands of people have already died from the disease. In the event of an outbreak like such, forecasting the extent of the mortality that will occur is crucial to aid the implementation of effective interventions. Mortality depends on two factors: the case fatality rate and the case incidence. We combine a cohort-based model that determines case fatality rates along with a modified logistic model that evaluates the case incidence to determine the number of deaths in all the US states over time; the model is also able to include the impact of interventions. Both models yield exceptional goodness-of-fit. The model predicted a range of death outcomes (79k to 246k) all of which are considerably greater than the figures presented in mainstream media. This model can be used more effectively than current models to estimate the number of deaths during an outbreak, allowing for better planning.

LABORATORY TESTS OF THYROID FUNCTION IN HYPERTHYROIDISM II.

1969 ◽  
Vol 62 (4_Suppla) ◽  
pp. S23-S35
Author(s):  
B.-A. Lamberg ◽  
O. P. Heinonen ◽  
K. Liewendahl ◽  
G. Kvist ◽  
M. Viherkoski ◽  
...  
Keyword(s):  
Thyroid Function ◽  
Laboratory Tests ◽  
Goodness Of Fit ◽  
Point Of View ◽  
Accurate Estimation ◽  
Diagnostic Efficiency ◽  
Normal Limits ◽  
Significant Difference ◽  
Crossing Point ◽  
Hyperthyroid Patients

ABSTRACT The distributions of 13 variables based on 10 laboratory tests measuring thyroid function were studied in euthyroid controls and in patients with toxic diffuse or toxic multinodular goitre. Density functions were fitted to the empirical data and the goodness of fit was evaluated by the use of the χ2-test. In a few instances there was a significant difference but the material available was in some respects too small to allow a very accurate estimation. The normal limits for each variable was defined by the 2.5 and 97.5 percentiles. It appears that in some instances these limits are too rigorous from the practical point of view. It is emphasized that the crossing point of the functions for euthyroid controls and hyperthyroid patients may be a better limit to use. In a preliminary analysis of the diagnostic efficiency the variables of total or free hormone concentration in the blood proved clearily superior to all other variables.

scholarly journals Guiding Residency Program Educational Goals Using Institutional Keyword Reports from the Ophthalmic Knowledge Assessment Program Examination

2020 ◽  
Vol 12 (02) ◽  
pp. e234-e238
Author(s):  
Isdin Oke ◽  
Steven D. Ness ◽  
Jean E. Ramsey ◽  
Nicole H. Siegel ◽  
Crandall E. Peeler
Keyword(s):  
Residency Program ◽  
High Variability ◽  
Knowledge Assessment ◽  
Data Driven ◽  
Educational Goals ◽  
Residency Programs ◽  
Percent Correct ◽  
Self Evaluation ◽  
Assessment Program ◽  
Significant Difference

Abstract Introduction Residency programs receive an institutional keyword report following the annual Ophthalmic Knowledge Assessment Program (OKAP) examination containing the raw number of incorrectly answered questions. Programs would benefit from a method to compare relative performance between subspecialty sections. We propose a technique of normalizing the keyword report to determine relative subspecialty strengths and weaknesses in trainee performance. Methods We retrospectively reviewed our institutional keyword reports from 2017 to 2019. We normalized the percentage of correctly answered questions for each postgraduate year (PGY) level by dividing the percent of correctly answered questions for each subspecialty by the percent correct across all subsections for that PGY level. We repeated this calculation for each PGY level in each subsection for each calendar year of analysis. Results There was a statistically significant difference in mean performance between the subspecialty sections (p = 0.038). We found above average performance in the Uveitis and Ocular Inflammation section (95% confidence interval [CI]: 1.02–1.18) and high variability of performance in the Clinical Optics section (95% CI: 0.76–1.34). Discussion The OKAP institutional keyword reports are extremely valuable for residency program self-evaluation. Performance normalized for PGY level and test year can reveal insightful trends into the relative strengths and weaknesses of trainee knowledge and guide data-driven curriculum improvement.

scholarly journals Bioelectric Effect of a Microcurrent Toothbrush on Plaque Removal

2021 ◽  
Vol 18 (16) ◽  
pp. 8255
Author(s):  
Ji-Hyun Lee ◽  
Jin-Hee Ha
Keyword(s):  
Bacterial Flora ◽  
Gingival Index ◽  
Direct Electric Current ◽  
Plaque Removal ◽  
Bioelectric Effect ◽  
Biofilm Removal ◽  
Significant Difference ◽  
The Us ◽  
Before And After ◽  
Previous State

This study evaluated the effectiveness of a microcurrent toothbrush (approved by the US Food and Drug Administration [FDA]), which employs a superimposed alternating and direct electric current, named as a Proxywave® technology, similar to the intensity of the biocurrent, in plaque removal and reducing gingivitis by biofilm removal through the bioelectric effect. This study enrolled 40 volunteers with gingivitis. Dental observations were made every two weeks, before and after the use of each toothbrush. We randomly assigned participants into two groups: one group used the Proxywave® toothbrush (PB) for two weeks followed by the control toothbrush (CB) for two weeks, while the other group used the CB for two weeks followed by the PB. The participants had a two-week washout period. If the toothbrush used earlier has had an effect on the bacterial flora in the oral cavity, this is to remove this effect and return it to its previous state. During each dental visit, we recorded plaque index (PI) and gingival index (GI) scores. The PI and GI scores were significantly lower in both the PB and the CB (p < 0.05). Considering the PI, there was no significant difference between the toothbrushes on all the surfaces. Considering the GI, the PB showed a significant decrease in the interproximal surface, compared to the CB (p < 0.05). The PB showed a significant decrease in the interproximal GI and had a beneficial effect in the interproximal area where the bristles could not reach. No adverse events were observed in the participants during the clinical trial. The microcurrent toothbrush is a device that can be safely used for plaque removal.

scholarly journals Assessing the Attitude of Tampa Bay Youth toward HIV Self-Testing Kits

2021 ◽  
Vol 6 (3) ◽  
pp. 111
Author(s):  
Sherry Zhang ◽  
Isabella Lopez ◽  
Bernard Washington ◽  
Brittney Gaudet ◽  
Carina A. Rodriguez ◽  
...  
Keyword(s):  
Tampa Bay ◽  
Hiv Status ◽  
Health Clinics ◽  
Home Testing ◽  
Self Testing ◽  
Significant Difference ◽  
The Us ◽  
Study Population ◽  
Location Study ◽  
Sexual Health Clinics

In adults, data support the utility and acceptance of home HIV testing; however, in youth, particularly in the US, this has not been well studied. In this exploratory study, we surveyed Tampa Bay youth aged 16−27 and attending sexual health clinics between 1 June and 31 June 2018 (n = 133) regarding attitudes and perceptions towards HIV self-testing. While most indicated the clinic over home when asked for preferred testing location, study population and subgroup analysis demonstrated a positive response (agree) to Likert-scale questions regarding the use of home HIV self-testing kits and negative responses (strongly disagree) to “would not use self-testing kit”. There was a significant difference between genders in testing location preference (p = 0.031) for those respondents that specified gender (n = 123), with males more likely to prefer home testing than females. This study suggests an openness of youth towards HIV home testing that could help to expand the number of youth aware of their HIV status.

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