Factors Associated With Disordered Eating Behavior Among Adolescent Girls: Screening and Education

Introduction/Objectives: An unhealthy relationship with food can lead to disordered eating in adolescence, highlighting the importance of screening. This study describes the frequency of disordered eating behavior among female adolescents, as well as associated characteristics and health behaviors. Methods: Data are from a multidimensional risk factor screening survey administered at a university medical center’s adolescent clinic from 2016 to 2018. The instrument was adapted from existing screening tools such as the Rapid Assessment for Adolescent Preventive Services (RAAPS), the American Medical Association’s Guidelines for Adolescent Preventive Services (GAPS), and the Youth Risk Behavior Survey (YRBS). Analysis was limited to self-reported responses provided by females aged 10 to 21 years (N = 915). Statistical analyses included chi-square tests and independent sample T-tests. Results: Of the N = 915 females who reported on disordered eating behavior, n = 57 (6.2%) had engaged in some form of disordered eating behavior within the past 12 months. Disordered eating was significantly associated ( P < .001) with not consistently wearing a helmet while biking, having tried e-cigarettes, being bullied in the past 30 days, having an adverse childhood experience (ACE), and being African American ( P = .005). Subgroup analysis of the relationship between disordered eating and bullying, by race, yielded significant findings: disordered eating was more highly associated with being bullied in the past 30 days among African American females ( P = .038). The relationship between disordered eating and ACE was also significant ( P < .001) among Caucasian girls when stratified by race. Conclusions: Adolescent risk behaviors often co-occur, and disordered eating behavior may be differentially observed by race. Findings highlight the importance of education and screening to prevent the development of disordered eating, and identify those who may be struggling. These results can be useful to community health education and in healthcare to develop and implement health promotion and eating disorder prevention strategies. Further studies are needed to assess additional factors that promote or protect against disordered eating to improve prevention.

Consumption of Alcoholic Beverages Associated With Physical Health Status in Adults: Secondary Analysis of the Health Information National Trends Survey Data

Introduction: Alcohol consumption constitutes one of the main modifiable risk factors that contribute to the increase in the global burden of non-communicable diseases (NCDs). The objective of this study was to determine the effects of the consumption of alcoholic beverages on the state of physical health and its equivalence according to gender. Methods: Cross-sectional data from the Health Information National Trends Survey (HINTS) of the National Cancer Institute (NCI) (n = 3865), collected during 2020 were used. Structural equation modeling was applied to assess the fit of the model, which included the prediction of measures of alcohol consumption in physical health and the equivalence of measurements of the proposed structural model in men and women. Results: The proposed structural model reported adequate goodness-of-fit indices (SBχ²/gl = 3.817, CFI = 0.984, TLI = 0.968, RMSEA [90% CI] = 0.027 [0.016-0.039]; SRMR = 0.016). Frequent alcohol consumption had a negative effect on physical health ( b = −0.13, P < .01). Similarly, occasional alcohol consumption negatively predicted elevated BMI and chronic conditions such as, diabetes, hypertension, CVD, and cancer ( b = −0.09, P < .01). In addition, drinking patterns of alcoholic beverages affect physical health in equal ways for men and women. Conclusion: The findings highlight that frequent and occasional alcohol consumption significantly affected physical health in a negative way. Future interventions could address ways to encourage the adoption of a healthy lifestyle to reduce the risks of chronic conditions derived from excessive alcohol consumption.

Symptoms COVID 19 Positive Vapers Compared to COVID 19 Positive Non-vapers

Objectives: The purpose of the present study was to assess and describe the severity of symptoms reported by Covid-19 positive patients who vaped (smoked e-cigarettes) when compared to those who did not vape or smoke at the time of the diagnosis of Covid-19. Methods: Patients from this study are from a well-characterized patient cohort collected at Mayo Clinic between March 1, 2020 and February 28, 2021; with confirmed COVID-19 diagnosis defined as a positive result on reverse-transcriptase–polymerase-chain-reaction (RT-PCR) assays from nasopharyngeal swab specimens. Among the 1734 eligible patients, 289 patients reported current vaping. The cohort of vapers (N = 289) was age and gender matched to 1445 covid-19 positive patients who did not vape. The data analyzed included: date of birth, gender, ethnicity, race, marital status, as well as lifestyle history such as vaping and smoking and reported covid-19 symptoms experienced. Results: A logistic regression analysis was performed separately for each symptom using generalized estimating equations (GEE) with robust variance estimates in order to account for the 1:5 age, sex, and race matched set study design. Patients who vaped and developed Covid-19 infection were more likely to have chest pain or tightness (16% vs 10%, vapers vs non vapers, P = .005), chills (25% vs 19%, vapers vs non vapers, P = .0016), myalgia (39% vs 32%, vapers vs non vapers, P = .004), headaches (49% vs 41% vapers vs non vapers, P = .026), anosmia/dysgeusia (37% vs 30%, vapers vs non vapers, P = .009), nausea/vomiting/abdominal pain (16% vs 10%, vapers vs non vapers, P = .003), diarrhea (16% vs 10%, vapers vs non vapers, P = .004), and non-severe light-headedness (16% vs 9%, vapers vs non vapers, P < .001). Conclusion: Vapers experience higher frequency of covid-19 related symptoms when compared with age and gender matched non-vapers. Further work should examine the impact vaping has on post-covid symptom experience.

Improving Child Development Screening: Implications for Professional Practice and Patient Equity

Introduction and Objectives: A pediatric group with 25 clinics and 150 providers used multifaceted approaches to implement workflow processes and an electronic health record (EHR) flowsheet to improve child developmental screening. The key outcome was developmental screening done for every patient during 3 periods between ages 8 and 36 months. Identification of developmental concerns was the secondary study outcome. Screening rates and referrals were hypothesized to be optimized for children regardless of demographic backgrounds. Methods: During preventive visits, developmental screens targeted patients in age groups 8 to 12, 13 to 24, and 25 to 36 months. EHRs were analyzed for screening documentation, results, and referrals by patient demographics. Fifteen pediatric professionals were interviewed about their qualitative experiences. Quality improvement interventions included appointing clinic champions, training staff about the screening process and responsibilities, using a standardized tool, employing plan-do-study-act cycles, posting EHR prompts, providing financial incentives, and monitoring screening rates using control charts. Results: Within 25 months, screening rates improved from 60% to >95% within the 3 preventive visit age groups for a total of more than 30 000 children. Professionals valued the team process improvements. Children enrolled in Medicaid, black children, and those living in lower income zip codes had lower screening rates than privately insured, white children, and those living in higher income areas. Ages and Stages Questionnaire 3rd edition results were significantly different by gender, race/ethnicity, insurance, and income categories across all groups. Referral rates varied by race/ethnicity and zip code of residence. Conclusions: This project resulted in an effective and efficient process to improve child developmental screening that was valued by pediatric professionals. Analyses of patient demographics revealed disparities in services for the most vulnerable families. Ongoing quality improvement, health services research, and advocacy offer hope to improve health equity.

Pathways of Teach-Back Communication to Health Outcomes Among Individuals With Diabetes: A Pathway Modeling

Teach-back method can help promote interactive communication between patients and providers. However, the mechanism of how teach-back operates in routine care is uninvestigated. Using pathway analysis, we explored the potential pathways of patient teach-back to health outcomes among individuals with diabetes. Study sample included 2901 US adults with diabetes ascertained from the 2011 to 2016 Longitudinal Medical Expenditure Panel Survey. Our pathway model analysis showed that patient teach-back was associated with better interaction with providers, shared decision-making, and receiving lifestyle advice. Teach-back had a direct negative effect on condition-specific hospitalization and indirect negative effects through lifestyle advice and diabetic complication. Teach-back method may promote active interactions between patients and providers by creating an opportunity to be more engaged in shared decision-making and receive additional health advice from providers. These improvements seem to be associated with a reduction in risks for complications and related hospitalization.

Industry 4.0 Technologies for the Manufacturing and Distribution of COVID-19 Vaccines

Background The evolutionary stages of manufacturing have led us to conceptualize the use of Industry 4.0 for COVID-19 (coronavirus disease 2019), powered by Industry 4.0 technologies. Using applications of integrated process optimizations reliant on digitized data, we propose novel intelligent networks along the vaccine value chain. Vaccine 4.0 may enable maintenance processes, streamline logistics, and enable optimal production of COVID-19 vaccines. Vaccine 4.0 Framework The challenge in applying Vaccine 4.0 includes the requirement of large-scale technologies for digitally transforming manufacturing, producing, rolling-out, and distributing vaccines. With our framework, Vaccine 4.0 analytics will target process performance, process development, process stability, compliance, quality assessment, and optimized maintenance. The benefits of digitization during and post the COVID-19 pandemic include first, the continual assurance of process control, and second, the efficacy of big-data analytics in streamlining set parameter limits. Digitization including big data-analytics may potentially improve the quality of large-scale vaccine production, profitability, and manufacturing processes. The path to Vaccine 4.0 will enhance vaccine quality, improve efficacy, and compliance with data-regulated requirements. Discussion Fiscal and logistical barriers are prevalent across resource-limited countries worldwide. The Vaccine 4.0 framework accounts for expected barriers of manufacturing and equitably distributing COVID-19 vaccines. With amalgamating big data analytics and biometrics, we enable the identification of vulnerable populations who are at higher risk of disease transmission. Artificial intelligence powered sensors and robotics support thermostable vaccine distribution in limited capacity regions, globally. Biosensors isolate COVID-19 vaccinations with low or limited efficacy. Finally, Vaccine 4.0 blockchain systems address low- and middle-income countries with limited distribution capacities. Conclusion Vaccine 4.0 is a viable framework to optimize manufacturing of vaccines during and post the COVID-19 pandemic.

A Covid -19 Virtual Ward Model: A Preliminary Retrospective Clinical Evaluation From a UK District General Hospital

Objective: This study aims to evaluate the safety, utilization, ability to reduce length of hospitalization and overall outcomes of a COVID-19 virtual ward providing ongoing treatment at home. Method: A retrospective single-center study of patients discharged to the COVID-19 virtual “step down” ward between January 27th 2021 and March 2nd 2021. The referral process, length of hospitalization, length of stay on the virtual ward, readmissions, and ongoing treatment requirements including supplemental oxygen, antibiotics, and/or steroids were all noted. Results: A total of 50 patients were referred to the virtual ward. 43 referrals were accepted, 39 of which were from the respiratory ward. Four patients were readmitted, all due to hypoxia. All readmissions occurred within 5 days of discharge. 72% (n = 31) were discharged home with an ongoing oxygen requirement. 14.3% of patients were discharged with antibiotics only, 9.5% with steroids only and 23.8% with both antibiotics and steroids. The mean length of hospital stay for patients discharged to the virtual ward was 10.3 ± 9.7 days and 11.9 ± 11.6 days for all covid positive patients during this time. On average, patients spent 13.7 ± 7.3 days on the virtual ward. The average number of days spent on oxygen on the virtual ward was 11.6 ± 6.0 days. Conclusion: The virtual ward model exemplifies the potential benefits of collaborative working between primary and secondary care services, relieving pressure on hospitals whilst providing ongoing treatments at home such as supplemental oxygen. It also facilitates an early supported discharge of clinically stable patients with an improving clinical trajectory by managing them in the community.

Three Years Survival and Factor Predicting Amputation or Mortality in Patients with High Risk for Diabetic Foot Ulcer in Fatmawati General Hospital, Jakarta

Background: Diabetic foot is one of major complication in diabetes patients with unfavorable outcome. Survival study in outpatients is limited and factors related are inconsistent. Survival and its modifiable risk factors should be identified early since the foot at risk status to reduce amputation/mortality in type 2 diabetes mellitus (T2DM). Objective: The aims of this study were to investigate survival probability for amputation or mortality, compare different ulcer risk classification, and figure out the relation of status of ulcer risk, age, gender, diabetes duration, body mass index, fasting plasma glucose, HbA1C, and LDL with amputation or mortality. Methods: This is a retrospective cohort study of 487 T2DM subjects who visited internal medicine outpatient clinic in Fatmawati General Hospital since January-December 2016. Status of ulcer risk and risk factors were extracted from medical record and lower-extremity amputation or mortality was observed in 3 years from baseline. Result: Three years overall survival is 85.7% (SE 0.17). Patients with high risk for foot ulcer have survival probability of 80.2% (SE 0.027), which is lower compared to non-high risk for foot ulcer with survival probability of 91.8% (SE 0.019). Patients with high risk for foot ulcer (aHR 2.386 [95% CI 1.356-4.20]; P = .003), aged ≥60 years old (aHR 2.051 [95% CI 1.173-3.585]; P = .012), and HbA1C ≥7% (aHR 2.022 [95% CI 1.067-3.830]; P = .031) were independently associated with amputation or mortality. Conclusion: T2DM patients with high risk for foot ulcer have lower survival probability and higher risk for amputation or mortality in 3 years compared to patients with non-high risk for foot ulcer. Status of ulcer risk, age ≥60 years, and HbA1C ≥7% were associated with amputation or mortality in 3 years observation.

A Case of Early Hereditary Transthyretin Amyloid Cardiomyopathy Recognition With Genetic Screening: A Case Report

Background: Transthyretin amyloid cardiomyopathy (ATTR-CM) is one of the most common types of cardiac amyloidosis. Amyloid cardiomyopathy more commonly affects men, elderly, and 3% to 4% of the African-American population. ATTR-CM suspicion and diagnosis is challenging; however, awareness of the disease is increasing, and best practices to identify it are being proposed. The approach to suspected cases of ATTR-CM relies on the presence of heart failure, red flag signs and symptoms, and age >65 or >70 for men and women respectively. Little is known about cases when it presents in early ages. Case: We report a 62-year-old African American male with past medical history of hyperlipidemia, prostate cancer, hypertension, bilateral carpal tunnel surgery that had debuted with a cardiac arrhythmia at age 55 and was diagnosed with heart failure several years later. Restrictive cardiomyopathy was suspected, and genetic screening was sent for ATTRm which confirmed a pathogenic trasnthyretin gene mutation. Endomyocardial biopsy was performed which confirmed cardiac amyloid deposition. Discussion: ATTR-CM is a rare disease with an increasing prevalence. Cases with out of proportion signs and symptoms of heart failure with preserved ejection fractions should raise the suspicion of ATTR-CM despite age.

Incidence of Obesity at 1 and 3 Years Among Community Dwelling Adults: A Population-Based Study

Objective: This study determined the incidence rates for obesity among adult patients ages 20 and older empaneled in primary care practice in Midwest United States to potentially identify an optimum timeframe for initiating intervention. Background: Primary care practice patients are likely to reflect underlying community trends in overweight and obesity; however, data on overweight and obesity in primary care patients is limited. While childhood incidence rates of obesity have been well reported, there is still a paucity of data on the incidence of obesity among adult population; literature has mainly focused on its prevalence. Methods: Medical record review of identified cohort with BMI data was conducted. Population was stratified by age and sex and overweight category was subdivided into tertiles. Results: Majority of 40 390 individuals who comprised the final population and had follow-up data, consisted of adults ages 40 to 69 years (47.5%), female (59.8%) of non-Hispanic ethnicity (95.9%) with 21 379 (52.8%) falling in weight category of overweight. Incidence of obesity was 7% at 1 year and 16% at 3 years follow-up. Highest percentages of individuals who became obese at 1 and 3 years were in age category of 40 to 69 years among men and 20 to 39 years among women. In Cox regression analysis, there was statistically significant association to developing obesity among all tertile groups in the overweight category. Age and particularly gender appeared to be modifying factors to likelihood of developing obesity. Conclusion: Study results suggest that while obesity incidence is higher among certain age groups in both genders, middle-aged women, and men in all tertiles of overweight category are at highest risk and may be the optimum population to target for weight loss interventions. Findings support the initiation of population-based interventions before onset of obesity.