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HOẶC
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tai lieu tong hop, thu vien luan van, luan van tong hop, do an chuyen nganh
Home visits in internal medicine graduate medical educationTÀI LIỆU NGÀNH MAY
Để xem full tài liệu Xin vui long liên hệ page để được hỗ trợ
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HOẶC
https://www.facebook.com/garmentspace/
https://www.facebook.com/thuvienluanvan01
https://www.facebook.com/thuvienluanvan01
tai lieu tong hop, thu vien luan van, luan van tong hop, do an chuyen nganh
Exploring the role of racial associations within life and psychiatry the diss...TÀI LIỆU NGÀNH MAY
Để xem full tài liệu Xin vui long liên hệ page để được hỗ trợ
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https://www.facebook.com/thuvienluanvan01
https://www.facebook.com/thuvienluanvan01
tai lieu tong hop, thu vien luan van, luan van tong hop, do an chuyen nganh
Factors influencing the receipt of diabetic retinopathy screening in a high r...TÀI LIỆU NGÀNH MAY
Để xem full tài liệu Xin vui long liên hệ page để được hỗ trợ
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https://www.facebook.com/thuvienluanvan01
https://www.facebook.com/thuvienluanvan01
tai lieu tong hop, thu vien luan van, luan van tong hop, do an chuyen nganh
A principal aim of epidemiology is to assess the cause of disease. However, since most epidemiological studies are by nature observational rather than experimental, a number of possible explanations for an observed association need to be considered before we can infer a cause-effect relationship exists.
The goal of this webinar was to help healthcare professionals improve care coordination for patients with advanced illness and to reduce hospital readmissions and length of stay.
Home visits in internal medicine graduate medical educationTÀI LIỆU NGÀNH MAY
Để xem full tài liệu Xin vui long liên hệ page để được hỗ trợ
: https://www.facebook.com/thuvienluanvan01
HOẶC
https://www.facebook.com/garmentspace/
https://www.facebook.com/thuvienluanvan01
https://www.facebook.com/thuvienluanvan01
tai lieu tong hop, thu vien luan van, luan van tong hop, do an chuyen nganh
Exploring the role of racial associations within life and psychiatry the diss...TÀI LIỆU NGÀNH MAY
Để xem full tài liệu Xin vui long liên hệ page để được hỗ trợ
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https://www.facebook.com/garmentspace/
https://www.facebook.com/thuvienluanvan01
https://www.facebook.com/thuvienluanvan01
tai lieu tong hop, thu vien luan van, luan van tong hop, do an chuyen nganh
Factors influencing the receipt of diabetic retinopathy screening in a high r...TÀI LIỆU NGÀNH MAY
Để xem full tài liệu Xin vui long liên hệ page để được hỗ trợ
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HOẶC
https://www.facebook.com/garmentspace/
https://www.facebook.com/thuvienluanvan01
https://www.facebook.com/thuvienluanvan01
tai lieu tong hop, thu vien luan van, luan van tong hop, do an chuyen nganh
A principal aim of epidemiology is to assess the cause of disease. However, since most epidemiological studies are by nature observational rather than experimental, a number of possible explanations for an observed association need to be considered before we can infer a cause-effect relationship exists.
The goal of this webinar was to help healthcare professionals improve care coordination for patients with advanced illness and to reduce hospital readmissions and length of stay.
Barriers to Access Quality Healthcare Services among Physically Challenged Pe...Premier Publishers
Despite the increase in the number of health services provided and Kenya’s commitment to equal access to quality healthcare for all by the year 2030, the physically challenged persons still find difficulty in accessing health services for reasons attributable to health care related factors. This study targeted the physically challenged persons in Gem Sub-county, Kenya. Stratified and systematic random sampling was used to select 108 people with physical disability. Data was collected using semi-structured questionnaire and analyzed using SPSS, version 23. Descriptive data were summarized in tables and charts while x2 test was used to detect the relationship between relevant variables (α= 0.05). This study confirmed that environmental accessibility of the hospitals, their location and infrastructure leading to the hospitals greatly influence ability of people with physical disabilities to access quality healthcare(p<0.05). All the healthcare facilities were not adequately equipped to handle people with disabilities. The healthcare system-related factors had influence negatively on access of quality care to the physically handicapped persons in Gem sub County.
Reducing Readmissions and Length of Stay | VITAS HealthcareVITAS Healthcare
Pain management is first and foremost in a hospice patient’s plan of care. Hospice provides comfort and quality of life near the end of life, and hospice providers are experts at managing pain. The goal of this webinar is to help healthcare professionals understand all aspects of a patient’s pain as a symptom near the end of life, and how to utilize an interdisciplinary approach to provide the most effective pain management.
Ian's UnityHealth 2019 grand rounds suicide preventionIan Dawe
At the end of this presentation, you will :
1. Knowledgeably describe the problem of suicide in our
clients as an issue beyond just the traditional targets of our
medical interventions,
2. Understand concepts of quality and process improvement
as they relate to implementation of suicide prevention
strategies in hospital and community settings,
3. Become a champion of the Project Nøw approach to improve
care and outcomes for individuals at risk of suicide in
healthcare systems locally, provincially and nationally.
Association of an Educational Program in Mindful Communication With Burnout, ...DAVID MALAM
Association of an Educational Program in Mindful Communication With Burnout, Empathy, and Attitudes Among Primary Care Physicians.
The consequences of burnout among practicing physicians include not only poorer quality of life and lower quality of care but also a decline in the stability of the physician workforce.
There has been a major decrease in the percentage of graduates entering careers in primary care in the last 20 years, with reasons related to burnout and poor quality of life. This trend, coupled with attrition among currently practicing physicians, have already had a significant effect on patient access to primary care services.
Replacing physicians who leave practice is expensive:
estimates are $250 000 or more per physician. Even though the problem of burnout in physicians has been recognized for years, there
have been few programs targeting burnout before it leads to personal or professional impairment and very little data exist about their effectiveness.
METHODS
Study Population
All primary care physicians in the Greater Rochester, New York, community
(N=871) were invited to participate in the program through a series of mailed and electronic communications from the Monroe County Medical Society to individual physicians and local health care organizations, with follow-up telephone calls from the investigators.
Patient Related Barriers Associated with Under Enrollment in Hospice: A ReviewQUESTJOURNAL
Background: Hospice care provides better quality of life compared with usual care, and focuses on caring, rather than curing. Many factors facing cancer patients at the last days of life prevent them from enrollment in hospice. Purpose:to identify the barriers associated with hospice under enrollment for terminally ill cancer patients. Methodology: an integrative literature review design was utilized, CINAHL, and PubMed were accessed by using key words (hospice, barriers, and cancer patients), and after applying inclusion criteria 8 articles were considered to meet the purpose of this review. Findings: through reviewing literatures,15% of hospice patients dis enrolled from hospice due to long-stay hospitalization, hospital death, & higher medicare expenditure with in sufficient insurance coverage (financial burden), and some other factors may contribute in under enrollment in hospice such as knowledge deficiency with misconception of hospice terminology and scope,mistrust of health care professionals, death timing, and some policies may create a barrier and restrict access to care for hospice. Conclusion:factors that may be associated with under enrollment of terminally ill cancer patients in hospice were lack of knowledge and misperception of hospice scope, emotional, physical and financial burden toward patient and family, death timing and bad quality of care
Factors associated to adherence to DR-TB treatment in Georgia, Policy Brief (...Ina Charkviani
Tuberculosis (TB) is a widely spread disease globally that causes millions of people’s death worldwide. Treatment for TB is complex and usually involves taking several antibiotics at once for a long time (sometimes up to two years). Considering the severity of the treatment regimen, it becomes hard for the patients to adhere and complete proposed treatment and particularly for those who are infected with drug-resistant strain of TB. Poor adherence to treatment remains significant problem that prevents countries from obtaining high treatment success rates that is essential for health systems to control the epidemic and decrease spread of the disease. A new study from Georgia looks at adherence to treatment factors among drug resistant TB (DR-TB) patients and provides evidence that may help policy-makers develop effective strategies for improving treatment outcomes among DR-TB patients. The study findings might be helpful for other countries in the region where TB burden is also high.
Barriers to Access Quality Healthcare Services among Physically Challenged Pe...Premier Publishers
Despite the increase in the number of health services provided and Kenya’s commitment to equal access to quality healthcare for all by the year 2030, the physically challenged persons still find difficulty in accessing health services for reasons attributable to health care related factors. This study targeted the physically challenged persons in Gem Sub-county, Kenya. Stratified and systematic random sampling was used to select 108 people with physical disability. Data was collected using semi-structured questionnaire and analyzed using SPSS, version 23. Descriptive data were summarized in tables and charts while x2 test was used to detect the relationship between relevant variables (α= 0.05). This study confirmed that environmental accessibility of the hospitals, their location and infrastructure leading to the hospitals greatly influence ability of people with physical disabilities to access quality healthcare(p<0.05). All the healthcare facilities were not adequately equipped to handle people with disabilities. The healthcare system-related factors had influence negatively on access of quality care to the physically handicapped persons in Gem sub County.
Reducing Readmissions and Length of Stay | VITAS HealthcareVITAS Healthcare
Pain management is first and foremost in a hospice patient’s plan of care. Hospice provides comfort and quality of life near the end of life, and hospice providers are experts at managing pain. The goal of this webinar is to help healthcare professionals understand all aspects of a patient’s pain as a symptom near the end of life, and how to utilize an interdisciplinary approach to provide the most effective pain management.
Ian's UnityHealth 2019 grand rounds suicide preventionIan Dawe
At the end of this presentation, you will :
1. Knowledgeably describe the problem of suicide in our
clients as an issue beyond just the traditional targets of our
medical interventions,
2. Understand concepts of quality and process improvement
as they relate to implementation of suicide prevention
strategies in hospital and community settings,
3. Become a champion of the Project Nøw approach to improve
care and outcomes for individuals at risk of suicide in
healthcare systems locally, provincially and nationally.
Association of an Educational Program in Mindful Communication With Burnout, ...DAVID MALAM
Association of an Educational Program in Mindful Communication With Burnout, Empathy, and Attitudes Among Primary Care Physicians.
The consequences of burnout among practicing physicians include not only poorer quality of life and lower quality of care but also a decline in the stability of the physician workforce.
There has been a major decrease in the percentage of graduates entering careers in primary care in the last 20 years, with reasons related to burnout and poor quality of life. This trend, coupled with attrition among currently practicing physicians, have already had a significant effect on patient access to primary care services.
Replacing physicians who leave practice is expensive:
estimates are $250 000 or more per physician. Even though the problem of burnout in physicians has been recognized for years, there
have been few programs targeting burnout before it leads to personal or professional impairment and very little data exist about their effectiveness.
METHODS
Study Population
All primary care physicians in the Greater Rochester, New York, community
(N=871) were invited to participate in the program through a series of mailed and electronic communications from the Monroe County Medical Society to individual physicians and local health care organizations, with follow-up telephone calls from the investigators.
Patient Related Barriers Associated with Under Enrollment in Hospice: A ReviewQUESTJOURNAL
Background: Hospice care provides better quality of life compared with usual care, and focuses on caring, rather than curing. Many factors facing cancer patients at the last days of life prevent them from enrollment in hospice. Purpose:to identify the barriers associated with hospice under enrollment for terminally ill cancer patients. Methodology: an integrative literature review design was utilized, CINAHL, and PubMed were accessed by using key words (hospice, barriers, and cancer patients), and after applying inclusion criteria 8 articles were considered to meet the purpose of this review. Findings: through reviewing literatures,15% of hospice patients dis enrolled from hospice due to long-stay hospitalization, hospital death, & higher medicare expenditure with in sufficient insurance coverage (financial burden), and some other factors may contribute in under enrollment in hospice such as knowledge deficiency with misconception of hospice terminology and scope,mistrust of health care professionals, death timing, and some policies may create a barrier and restrict access to care for hospice. Conclusion:factors that may be associated with under enrollment of terminally ill cancer patients in hospice were lack of knowledge and misperception of hospice scope, emotional, physical and financial burden toward patient and family, death timing and bad quality of care
Factors associated to adherence to DR-TB treatment in Georgia, Policy Brief (...Ina Charkviani
Tuberculosis (TB) is a widely spread disease globally that causes millions of people’s death worldwide. Treatment for TB is complex and usually involves taking several antibiotics at once for a long time (sometimes up to two years). Considering the severity of the treatment regimen, it becomes hard for the patients to adhere and complete proposed treatment and particularly for those who are infected with drug-resistant strain of TB. Poor adherence to treatment remains significant problem that prevents countries from obtaining high treatment success rates that is essential for health systems to control the epidemic and decrease spread of the disease. A new study from Georgia looks at adherence to treatment factors among drug resistant TB (DR-TB) patients and provides evidence that may help policy-makers develop effective strategies for improving treatment outcomes among DR-TB patients. The study findings might be helpful for other countries in the region where TB burden is also high.
EVIDENCE-BASED PRACTICES & NURSING 2
Evidence-Based Practices & Nursing
Walden University
NURS-6052N-37, Essent of Evidence-Based Practices
Literature Review
Increase in cases of breast cancer has been a major challenge in the healthcare setting. Additionally, treatments for patients suffering from breast cancer have been inadequate and this has created a major crisis thus leading bad health conditions for people living with breast cancer. Additionally, there has been an alarming increase in the number of patients suffering from breast cancer. This particular question on why the levels of breast cancer have increased has not been answered since this particular disease has been termed as being very complicated to understand mainly because its causes are many. However, this increase in the number of breast cancer cases has been attributed to a number of factors. To start with, screening of breast cancer has been a reason for the rise in breast cancer cases. Previously women were never screened for this particular disease. However, with the introduction of new technology screening has become a common thing. Screening has helped in the detection of breast cancer especially during the early years of development. Therefore anytime there is a screening clinic it is expected that the number of women suffering from the breast will increase.
Secondly, lifestyle changes have also attributed to the rise in breast cancer cases. In the modern day world, women have greatly changed their lifestyles this has been attributed to the growth of their careers and also an increase in women’s income. These changes in status have made women to change their lifestyles this means that they can afford to spend more on leisure and thus engage in activities such as alcoholism. Alcohol is one of the major cause of breast cancer among women also it is known to increase the chances of breast cancer in women. Statistically, it has been proven that an increase in the units of drinking by a woman can lead to an increase in the risk of getting breast cancer. Additionally, eating fast foods has also been a major attribute of breast cancer. Women who tend to have excess weight are at a higher risk of getting cancer when compared to women who have less weight (Polit ,& Beck, 2017).
Other factors that have been known to increase the chances of breast cancer in women include; hormone replacement therapy. The use of hormone replacement therapy has been known to be a predisposing factor for breast cancer. According to research, it was found out that the more the woman uses HRT the more the chances of getting breast cancer. Additionally, women who tend to have children during their late age are at a higher risk of getting cancer when compared to those who get children at an early age. This particular aspect explains why women in less developed countries are less prone to breast cancer. Age ...
Epidemiological studies are applicable to communicable and non-com.docxSALU18
Epidemiological studies are applicable to communicable and non-communicable diseases. Childhood obesity is an area that is receiving more attention in public health due to the multiple morbidities that emerge as a result of this condition. Below are links to a cross-sectional study and a case-control study. Imagine that you are interested in conducting a case-control or cross-sectional study proposal of childhood obesity vs. birth weight (prenatal and early life influences). Both articles below address prenatal influences on childhood obesity and birth weight using different approaches.
Article 1 -attached
Article 2-attached
Using the information in the articles, answer the following questions using AMA format.
1. How would you select cases and controls for this study and how would you define exposure and outcome variables for a case-control study design? What other factors would you control for?
2. How would you design a proposal measuring the effect of birthweight on childhood obesity for a cross-sectional study design? What other factors would you control for?
BioMed CentralBMC Public Health
ss
Open AcceStudy protocol
Cross sectional study of childhood obesity and prevalence of risk
factors for cardiovascular disease and diabetes in children aged 11–
13
Anwen Rees*1, Non Thomas1, Sinead Brophy2, Gareth Knox1 and
Rhys Williams2
Address: 1Cardiff School of Sport, University of Wales Institute Cardiff, Wales, UK and 2School of Medicine, Swansea University, Wales, UK
Email: Anwen Rees* - [email protected]; Non Thomas - [email protected]; Sinead Brophy - [email protected];
Gareth Knox - [email protected]; Rhys Williams - [email protected]
* Corresponding author
Abstract
Background: Childhood obesity levels are rising with estimates suggesting that around one in
three children in Western countries are overweight. People from lower socioeconomic status and
ethnic minority backgrounds are at higher risk of obesity and subsequent CVD and diabetes.
Within this study we examine the prevalence of risk factors for CVD and diabetes (obesity,
hypercholesterolemia, hypertension) and examine factors associated with the presence of these
risk factors in school children aged 11–13.
Methods and design: Participants will be recruited from schools across South Wales. Schools
will be selected based on catchment area, recruiting those with high ethnic minority or deprived
catchment areas. Data collection will take place during the PE lessons and on school premises. Data
will include: anthropometrical variables (height, weight, waist, hip and neck circumferences, skinfold
thickness at 4 sites), physiological variables (blood pressure and aerobic fitness (20 metre multi
stage fitness test (20 MSFT)), diet (self-reported seven-day food diary), physical activity (Physical
Activity Questionnire for Adolescents (PAQ-A), accelerometery) and blood tests (fasting glucose,
insulin, lipids, fibrinogen (Fg), adiponectin (high molecular weight), C-react ...
Aene project a medium city public students obesity studyCIRINEU COSTA
Identifying undernutrition and obesity on students and propose public policies of health are urgent issues. This paper presents a study with weight and stature from students collected by physical education teachers (PEF) in schools of a city near São Paulo. The PEF collected the data and they were inserted in a program especially developed for each school Department (AENE Project). The datas were analyzed by software and evaluation done based on a World Health Organization (WHO_2007) table, that develops health programs worldwide. The results evaluations were used to raise the students and family, teachers and responsibles for treatment search (when required).
Running head CONGENITAL ANOMALIES 1 Congenital Anoma.docxtodd271
Running head: CONGENITAL ANOMALIES 1
Congenital Anomalies
Jillian Zucco
Regis College, PMHNP
CONGENITAL ANOMALIES 2
Congenital Anomalies
The purpose of this assignment is to critique an article about a topic covered in this
week’s reading material and discuss both the topic and the article with classmates. The topic I
chose for this assignment is: congenital anomalies. Congenital anomalies are genetic or inherited
disorders or developmental disorders that are present at birth. A congenital anomaly can be
caused by a single-gene disorder, which is a mutation in one gene in the ova or sperm that is
passed down to later generations. Mutations in body cells that are not reproductive cells can
cause a disorder or dysfunction but cannot be passed down the way mutations in reproductive
cells can (Vanmeter, 2014).
Chromosomal defects can also be the cause congenital anomalies. During meiosis, DNA
fragments can be displaced or lost. This kind of error is what usually causes chromosomal
anomalies and is more common when the mother is older than age 35. Some congenital
disorders happen at birth, but do not have a genetic component. These can occur from premature
birth, exposure to teratogenic agents, or a traumatic labor or delivery. Teratogenic agents are
those that can damage the embryo or fetus and its development. Some congenital anomalies are
caused by multiple genes, making them polygenic disorders (Vanmeter, 2014).
The article I chose to critique is entitled “Dietary glycemic index and glycemic load
during pregnancy and offspring risk of congenital heart defects: a prospective cohort study.” It
was authored by Amalie Schmidt, Marie Lund, Giulia Corn, Thorhallur Halldorsson, Nina Oyen,
Jan Wohfahrt, Sjurdur Olsen, and Mads Melbye, all of whom are affiliated with reputable
institutions, such as the University of Bergen Department of Global Public Health, Harvard TH
Chan School of Public Health, and Stanford University School of Medicine. The article was
published this year, 2020, in The American Journal of Clinical Nutrition. The purpose of this
CONGENITAL ANOMALIES 3
article was research- to examine the relationship between mid-pregnancy dietary glycemic index,
glycemic load, and the risk of congenital heart defects in the baby. The article does not include a
formal literature review, but the introduction section provides information already published
about the topic from previous studies, mostly in the discipline of medicine. The journals cited
from are mostly medical journals on the topics of pediatrics, epidemiology, and diabetes. The
authors identify a research gap by stating that only one other study exists that assesses the risks
between glycemic index and heart defects. The first aim of the study was to investigate the
association between glycemic index and glycemic load during pregnancy and offspring risk of
congenital heart defects using a food-fre.
1Rough Draft Quantitative Research Critique And Ethical Consider.docxaulasnilda
1
Rough Draft Quantitative Research Critique And Ethical Considerations
Rough Draft Quantitative Research Critique And Ethical Considerations 2
Quantitative Research Critique and Ethical Considerations
Christiana Bona
Grand Canyon University
October 16, 2019
Childhood Obesity
Picot Question: How do the new practical approaches in the diagnosis and management help in the reduction of obesity issues as compared to the lack of reliance on the traditional approaches (diet and exercise)?
Background
Childhood obesity is considered to be a major international growing problem. Even though there is a high rate of obesity cases in the United States and other regions of the world for the past 30 years, there is still a lack of clear treatment approaches to be applied to reduce this incidence. Healthcare providers are lacking ideas on where they should find approaches related to the guidance and the process of managing the healthcare services to about one-third of the population who are affected by the healthcare issues associated with obesity. The Pediatric Obesity Algorithm is now considered to be an evidence-based approach in terms of diagnosing and managing childhood obesity. The authors of this study aimed at providing a summary of the topics from the Pediatric Obesity Algorithm related to the diagnosis of pediatric obesity, assessment, and management. Other tasks include the performance of differential diagnosis, reviewing the systems, diagnosis of the workup, physical examinations, management of age-specific, and the treatment of the associated weight gain. The outcome of this study will, therefore, help in the identification and treatment of children with obesity through using the Pediatric Obesity Algorithm which serves as a guide to healthcare providers with evidence-based approaches to the diagnosis process and managing obesity in children and offering families with the tools they require to ensure that there is healthy future.
How the article is supporting the topic
The article above is talking about some of the present gaps concerning the management and treatment of obesity among children. Through identifying that there is an absence of clear treatment approaches that can be relied on the treat and manage obesity among children, the authors of this article proposed that the Pediatric Obesity Algorithm can be successful in providing evidence-based approaches for diagnosing and amanging childhood obesity. Therefore, Pediatric Obesity Algorithm can be considered as a new approach in managing and diagnosisng obesity among children as compared to the traditional methods that involve watching the diet and exercise which is sometimes not followed by children. Pediatric Obesity Algorithm approach is helping in ensuring that there is a proper physical examination, age-specific management, use of medication and surgery, and performance of differential diagnosis among other procedures that are performed in this approach.
Method of ...
Childhood obesity has been described as the main health-related problem in developed countries, due to its link with physical, social and psychological consequences with an increased risk for developing metabolic and cardiovascular diseases in adulthood.
All the pupils of both sexes attending the second year of all the primary schools in Pavia, Northern Italy, were recruited (n=470) for this study. Measurements of weight, height and waist circumference (WC) were taken under standard conditions. Body Mass Index (BMI) and waist-to-height-ratio (W/HtR) were computed and sex specific percentile values for BMI, WC and W/HtR were calculated and compared with the same percentiles available for different countries.
The results show that according to Cole’s cut-off point reference standards, 12.5% and 9.0% of boys and girls respectively are overweight, 4.7% and 5.2% respectively are obese. The WC mean value is equal to 60.0 ± 6.0 cm in boys and 59.0 ± 6.7 cm in girls. Using different 90th reference worldwide standard percentiles for WC as a comparison, the prevalence of our children with WC > 90th percentile is very different. The W/HtR mean value of the total sample is 0.46 ± 0.03. Assuming a cutoff of 0.5, 87.6% of the pupils have a W/HtR value ≤ 0.5, while 12.4% of the subjects have a value > 0.5, showing abdominal obesity among 55 children at an early age.
Our results point out the need for specific preventive and treatment interventions by identifying and implementing effective strategies, policies, and nutritional education programs in order to decrease the prevalence rate of obesity as well as the risk of metabolic disorders.
nejm obesidad en adolescente. 2102062.pdfmedineumo
obesidad en adolescente: suscríbase a nuestro canal de YouTube _MediNeumo_
La obesidad durante la adolescencia (10 a 19 años de edad) está asociada con consecuencias para la salud que incluyen prediabetes y diabetes tipo 2, enfermedad del hígado graso no alcohólico, dislipidemia, síndrome de ovario poliquístico (SOP), apnea obstructiva del sueño, y salud mental trastornos y estigma social. demás, la obesidad durante la adolescencia es un factor de riesgo de complicaciones y muerte por enfermedad coronaria , así como de muerte por cualquier causa en la edad adulta, incluida la edad adulta temprana.
ABSTRACT- Background: Malnutrition constitutes a major public health concern worldwide and serves as an indicator
of hospitalized patient’s prognosis. Nutritional support is an essential aspect of the clinical management of children
admitted to hospital. Malnutrition has been long associated with poor quality, poor diet and inadequate access to health
care, and it remains a key global health issue that both stems from and contributes to weakness, with 50% of childhood
deaths due to principal under nutrition.
Methods: The present hospital based cross sectional study was conducted in April to Dec 2015 among 300 rural
adolescents of 9-18 years age (146 boys and 154 girls) attending the outpatient department at Patna Medical College and
Hospital, Bihar, India, belonging to the all caste communities. The nutritional status was assessed in terms of under
nutrition (weight-for-age below 3rd percentile), stunting (Height-for-age below 3rd percentile) and thinness (BMI-for-age
below 5th percentile). Diseases were accepted as such as diagnosed by pediatrician, skin specialist and medical officer.
Results: The prevalence of underweight, stunting and thinness were found to be 31%, 22.3% and 30.7% respectively. The
maximum prevalence of malnutrition was observed among early adolescents (23% - 54%) and the most common
morbidities were diarrhoea (16.7%), carbuncle / furuncle (16.7%) and scabies (12%).
Conclusion: Malnutrition among hospitalized under five children and around suffers moderately high rates of
malnutrition. Present nutrition programs attention on education for at risk children and referral to regional hospitals for
malnourished children. Screening tools to classify children at risk of developing malnutrition might be helpful.
Key-words- Malnutrition, Hospitalized children, Morbidities, Prevalence, Stunting
Synthetic Fiber Construction in lab .pptxPavel ( NSTU)
Synthetic fiber production is a fascinating and complex field that blends chemistry, engineering, and environmental science. By understanding these aspects, students can gain a comprehensive view of synthetic fiber production, its impact on society and the environment, and the potential for future innovations. Synthetic fibers play a crucial role in modern society, impacting various aspects of daily life, industry, and the environment. ynthetic fibers are integral to modern life, offering a range of benefits from cost-effectiveness and versatility to innovative applications and performance characteristics. While they pose environmental challenges, ongoing research and development aim to create more sustainable and eco-friendly alternatives. Understanding the importance of synthetic fibers helps in appreciating their role in the economy, industry, and daily life, while also emphasizing the need for sustainable practices and innovation.
Biological screening of herbal drugs: Introduction and Need for
Phyto-Pharmacological Screening, New Strategies for evaluating
Natural Products, In vitro evaluation techniques for Antioxidants, Antimicrobial and Anticancer drugs. In vivo evaluation techniques
for Anti-inflammatory, Antiulcer, Anticancer, Wound healing, Antidiabetic, Hepatoprotective, Cardio protective, Diuretics and
Antifertility, Toxicity studies as per OECD guidelines
How to Make a Field invisible in Odoo 17Celine George
It is possible to hide or invisible some fields in odoo. Commonly using “invisible” attribute in the field definition to invisible the fields. This slide will show how to make a field invisible in odoo 17.
Palestine last event orientationfvgnh .pptxRaedMohamed3
An EFL lesson about the current events in Palestine. It is intended to be for intermediate students who wish to increase their listening skills through a short lesson in power point.
2024.06.01 Introducing a competency framework for languag learning materials ...Sandy Millin
http://sandymillin.wordpress.com/iateflwebinar2024
Published classroom materials form the basis of syllabuses, drive teacher professional development, and have a potentially huge influence on learners, teachers and education systems. All teachers also create their own materials, whether a few sentences on a blackboard, a highly-structured fully-realised online course, or anything in between. Despite this, the knowledge and skills needed to create effective language learning materials are rarely part of teacher training, and are mostly learnt by trial and error.
Knowledge and skills frameworks, generally called competency frameworks, for ELT teachers, trainers and managers have existed for a few years now. However, until I created one for my MA dissertation, there wasn’t one drawing together what we need to know and do to be able to effectively produce language learning materials.
This webinar will introduce you to my framework, highlighting the key competencies I identified from my research. It will also show how anybody involved in language teaching (any language, not just English!), teacher training, managing schools or developing language learning materials can benefit from using the framework.
Macroeconomics- Movie Location
This will be used as part of your Personal Professional Portfolio once graded.
Objective:
Prepare a presentation or a paper using research, basic comparative analysis, data organization and application of economic information. You will make an informed assessment of an economic climate outside of the United States to accomplish an entertainment industry objective.
The Roman Empire A Historical Colossus.pdfkaushalkr1407
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External validation of an electronic phenotyping algorithm to detect attention to elevated bmi and weight related
1. Yale University
Yale University
EliScholar – A Digital Platform for Scholarly Publishing at Yale
EliScholar – A Digital Platform for Scholarly Publishing at Yale
Yale Medicine Thesis Digital Library School of Medicine
January 2020
External Validation Of An Electronic Phenotyping Algorithm To
External Validation Of An Electronic Phenotyping Algorithm To
Detect Attention To Elevated Bmi And Weight-Related
Detect Attention To Elevated Bmi And Weight-Related
Comorbidities In Pediatric Primary Care.
Comorbidities In Pediatric Primary Care.
Anya Golkowski Barron
Follow this and additional works at: https://elischolar.library.yale.edu/ymtdl
Recommended Citation
Recommended Citation
Golkowski Barron, Anya, "External Validation Of An Electronic Phenotyping Algorithm To Detect Attention
To Elevated Bmi And Weight-Related Comorbidities In Pediatric Primary Care." (2020). Yale Medicine
Thesis Digital Library. 3905.
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2. External Validation of an Electronic Phenotyping Algorithm to Detect Attention to
Elevated BMI and Weight-Related Comorbidities in Pediatric Primary Care
A Thesis Submitted to the
Yale University School of Medicine
in Partial Fulfillment of the Requirements for the
Degree of Doctor of Medicine
By
Anya Golkowski Barron
2020
3. ii
ABSTRACT
External Validation of an Electronic Phenotyping Algorithm to Detect Attention to
Elevated BMI and Weight-Related Comorbidities in Pediatric Primary Care.
Anya Golkowski Barron1
, Christy Turer 2
, Ada Fenick1
, Kaitlin Maciejewski1
, and Mona
Sharifi1
.
1
Department of Pediatrics, Yale University, School of Medicine, New Haven, CT.
2
Department of Pediatrics, University of Texas Southwestern Medical Center and Children’s Health, Dallas, TX.
Pediatric obesity is a growing national and global concern with nearly 1 in 5
children in the U.S. affected [1].The American Academy of Pediatrics endorsed expert
committee recommendations in 2007 to assist clinicians in pediatric weight management;
however, adherence to these recommendations among primary care providers is
suboptimal, and measuring adherence in feasible and pragmatic ways is challenging[2-4].
Commonly used quality measures that rely on billing data alone are an inadequate
measure of provider attention to weight status in pediatric populations as they do not
capture whether providers communicate about elevated body mass index (BMI) and
associated medical risks with families. Electronic phenotyping is a unique tool that has
the ability to use multiple areas of stored clinical data to group individuals according to
pre-defined characteristics such as diagnostic codes, laboratory values or medications.
We examined the external validity of a phenotyping algorithm, developed previously by
Turer et al and validated in a single health system in Texas, that assesses pediatric
providers’ attention to obesity and overweight using structured data from the electronic
health record (EHR), to three pediatric primary care practices affiliated with Yale New
Haven Health. Well child visit encounters were labeled either “no attention”, “attention to
BMI only”, “attention to comorbidity only,” or “attention to BMI and comorbidity”. The
performance of the algorithm was evaluated on the ability to predict “no attention”, using
4. iii
chart review as the reference standard. The application of the minimally altered algorithm
yielded a sensitivity of 94.0% and a specificity of 79.2% for predicting “no attention”,
compared to a sensitivity of 97.9% and a specificity of 94.8% in the original study. Our
findings suggest that while electronic phenotyping using structured EHR inputs provides
a better evaluation of clinic encounters than use of diagnostic codes alone, methods that
incorporate information in unstructured (“free text”) clinical notes may yield better
results.
5. iv
Acknowledgements
To Julian and Brian for being the salt and light of my world.
To my parents for being the giants whose shoulders I stand on.
To Dr. Mona Sharifi the office of student research and the department of Pediatrics
without whom this work would not be possible.
6. v
TABLE OF CONTENTS
1. Abstract…………………………………………………………………….. ii
2. Acknowledgements………………………………………………………….iv
3. Introduction…………………………………………………………………..1
a. Definitions of Pediatric Overweight and Obesity………………………... 1
b. What does Pediatric Overweight and Obesity look like in the US………. 2
c. Current Guidelines on Addressing Pediatric Obesity……………………. 3
d. Current Practice vs. Guidelines……………………………………........... 5
e. Methods of Assessing Provider Attention to Pediatric Weight Status…... 6
4. Statement of Purpose……………………………………………………….. 8
5. Methods……………………………………………………………………...10
6. Results……………………………………………………………………….18
7. Discussion…………………………………………………………………...23
8. References…………………………………………………………………...26
7. INTRODUCTION
1. Definitions of Pediatric Overweight and Obesity
Overweight and obesity are clinical terms used to denote excess body weight, most
frequently thought of in the form of adipose tissue. A commonly used measure for
estimating body fat percentages in medicine is body mass index (BMI). BMI provides a
measure of body weight adjusted for height, and although it does not provide a direct
measure of body fat, levels do correlate with and are predictive of future adiposity [5].
BMI is also clinically useful as it can easily be assessed in the primary care setting with
routine measurements of height and weight as opposed to more precise but less feasible
methods such as dual-energy x-ray absorptiometry. Given the nature of the calculation,
BMI may overestimate adiposity in children who have shorter statures or higher muscle
mass and may underestimate adiposity in children with very low muscle mass. However,
given its low cost, clinical utility and practicality, it is broadly used in clinical
environments. It is therefore applied as an initial screen in assessing a patient’s risk for
obesity and obesity-related comorbidities. Due to the fact that children’s BMI
measurements change dramatically with age and differ with sex, age-and sex-specific
BMI percentiles based on the Center for Disease Control (CDC) growth charts are used in
place of raw BMI values[6]. Cutoff points for increased health risks are defined
according to the 2007 expert committee recommendations convened by the department of
Health and Human Services[5]. These guidelines suggest that a BMI of less than the 85th
percentile is unlikely to pose health risk, whereas a BMI greater than or equal to the 95th
percentile would confer significant risk. The terms “overweight” are therefore applied to
a BMI ³ 85th
percentile and “obesity” to a BMI ³ the 95th
percentile. While the CDC
8. 2
growth charts are useful for a large percentage of patients with overweight and obesity,
BMI percentiles beyond the 97th
percentile are not clinically useful, as large changes in
BMI result in small percentile changes at the extreme. Therefore an additional metric,
percentage of BMI at the 95th
percentile (%BMIp95), is used to better assess and follow
patients with severe obesity, defined as a BMI greater than or equal to 120% of the 95th
percentile for age and sex[7].
2. What Does Pediatric Obesity and Overweight look like in the US?
On a population level, obesity disproportionately affects children from
racial/ethnic minority backgrounds. African-American and Latino children display higher
BMI scores from a young age and maintain a higher BMI growth trajectory compared to
their non-Hispanic White counterparts [8]. According to some studies looking at
disparities in obesity prevalence, obesity seems to emerge and is sustained earlier in
Hispanic children relative to African Americans, but both groups experience higher BMIs
by the 8th
grade relative to non-Hispanic White children[9]. The morbidities associated
obesity, such as hypertension and type II diabetes, are also disproportionately diagnosed
in minority children and tend to be seen more in boys [10]. Having diseases such as
elevated blood pressure or diabetes in childhood confers further risk of these diseases
carrying on into adulthood and increases overall risk of mortality from cardiovascular or
metabolic diseases [10, 11].
The risk factors associated with obesity are complex and intertwined. In general,
poverty is positively associated with obesity prevalence[5]. There is evidence that genes
play a role in obesity risk, and having one or both parents with obesity, increases the risk
of a child developing obesity significantly [12]. However, the rapid increase in
9. 3
prevalence at a population level suggests that environmental factors play a greater role
than genetic shifts in the population[11]. Many associations with obesity risk such as
infant birth weight, increased screen time, sleep patterns, and neighborhood-level factors
have been described, but their interdependence and individual contribution to a patient’s
risk are largely undefined, making prediction, and prevention particularly difficult[6, 13-
16].
Childhood obesity and overweight have shown to be predictors of future obesity,
putting patients at risk for the eventual development of obesity-related comorbidities.[17]
The medical complications of obesity are far reaching and include a range of life altering
disorders including hypertension, diabetes mellitus, non-alcoholic fatty liver disease,
dyslipidemia, asthma, and sleep apnea[18]. Managing these co-morbidities incur
significant cost to individual patients and healthcare systems. One study estimates the
lifetime cost for elementary students aged 6-11 with obesity to be $31,869 for boys and
$39,815 for girls due mainly to the care required for comorbidity management [19].
3. Current Guidelines on Addressing Pediatric Obesity
In 2007, an expert committee was formed to revise the 1998 recommendations on
childhood obesity. The recommendations were rooted in the latest evidence-based data
and the experience of clinical experts to address prevention, assessment, and treatment of
childhood overweight and obesity. The guidelines suggest that all children ages 2 years
and older be screened with initial BMI measurements, family history of obesity and
obesity-related disorders, and current diet and lifestyle practices. If a patient has a BMI
that is ≥85th
percentile, the first steps a provider should take are to assess the medical and
behavioral risks of the individual patient. Medical risk assessment includes screening for
10. 4
common comorbid conditions such as hypertension, type 2 diabetes, hyperlipidemia, and
non-alcoholic fatty liver disease. It was recommended by the committee that laboratory
tests to screen for and diagnose such conditions be conducted every 2 years for children
ages 10 years and older with obesity (or with overweight if they have associated risk
factors) [5]. Behavioral assessment includes identifying obesogenic behaviors such as
elevated screen time, fast food consumption, sugar-sweetened beverage intake, and
sedentary lifestyle. Providers should then take steps to address overweight and obesity,
and the guidelines make suggestions of four different treatment stages. These stages are:
stage 1 prevention plus, stage 2 structured weight management, stage 3 comprehensive
multidisciplinary approach and stage 4 tertiary care intervention. Each stage builds from
office-based counseling for lifestyle and family recommendations (stage 1) to nutrition
and psychological counseling (stages 2 and 3). Stage 4 uses interventions such as
medications, very low calorie diets, and bariatric surgery[5]. In cases of a child not
reaching a desired weight goal or in the presence of significant comorbidities,
pharmacotherapy can be considered. Orlistat is the only FDA approved medication for
the treatment of overweight and obesity in adolescents. Moderate improvements in BMI
have been associated with the use of Orlistat however, unlike in adult counterparts,
improvement in lipids or insulin sensitivity have not been consistently shown. Metformin
has also shown some ability to improve BMI in some short-term obesity studies when
used in conjunction with lifestyle modifications. Reported results on lipid and insulin
sensitivity have been variable and Metformin is not FDA approved for weight reduction
in pediatric patients [20].
4. Current Practice vs. Guidelines
11. 5
Pediatric primary care providers (PCPs) are the cornerstone of addressing
pediatric obesity as many successful interventions rely on PCPs to screen for and manage
children with elevated BMI [21, 22]. Studies report that patients and families see their
primary care provider as a reliable source of information and their recommendations have
positive impacts on weight management [23, 24]. However, suboptimal rates of diagnosis
of overweight and obesity based on BMI percentile in pediatric primary care persist [25,
26]. One 2011 study based on self-reported practice, found that less than 50% of primary
care providers assessed BMI regularly in children and 58% reported rarely, or only
sometimes using BMI percentiles to track weight [27]. Another 2011 study, found that
pediatric providers reported unfamiliarity with the 2007 practice guidelines and
diagnostic criteria for overweight and obesity suggesting that uptake of new practices has
been slow[28]. Use of the Electronic Health Record (EHR) imparts the ability to auto-
calculate BMI percentiles theoretically improving provider attention and diagnosis. Yet,
despite some improvement with broad implementations of the EHR, children with
overweight and obesity are still underdiagnosed[25, 29]. Counseling behaviors amongst
providers have also been shown to be variable depending on factors such as sex, personal
beliefs and attitudes[25]. In particular, younger children (2-5 years old) and children
with overweight are more likely to be underdiagnosed, not receive diet and exercise
counseling and have an absence of screening studies [2, 25, 30]. Perceived barriers to
providing adequate care are often reported to be the sensitivity of the topic, clinic time
constraints, and feelings of futility [3, 31, 32]. These inconsistencies across providers
present missed opportunities to engage with families early, influence BMI trajectories,
and provide high-quality care.
12. 6
5. Methods of Assessing Provider Attention to Pediatric Weight Status
Given the growing need for PCP attention to childhood obesity and the
suboptimal rates of diagnosis and screening, it is important to identify methods to support
clinicians in this task. Broad use of the EHR puts researchers in a position to easily
collect large amounts of data regarding physician practice. While manual chart review is
still widely done, the process is laborious and may often limit sample sizes. Electronic
phenotyping involves automated identification of subjects based on exclusion and
inclusion criteria present in stored clinical data. Electronic phenotyping is typically used
to identify patients with certain characteristics for a given purpose i.e.; a clinical trial, or
retrospective study. In a study published in 2018 titled, “Algorithm to detect pediatric
provider attention to high BMI and associated medical risk,” Dr. Christy Turer and
colleagues developed an electronic phenotyping algorithm using extractable EHR
variables to indicate adherence to the 2007 expert committee guidelines on childhood
obesity. Using diagnostic codes, laboratory studies, referrals, medications and procedures
they categorized provider behavior in response to elevated BMI measurements into one
of three phenotypes: “no attention”, attention to “BMI Alone”, and attention to
“BMI/Medical Risk”. Validation of the performance of the electronic phenotypes using
manual chart review showed excellent sensitivity and specificity to detect provider
attention types in pediatric clinics in Dallas, Texas[33]. By employing an algorithm to
evaluate clinician behavior, Turer et al created a tool that went beyond identifying
patients with disease characteristics to identifying encounters that follow guideline-based
care. Furthermore, a follow up study published in 2019 by Turer et. al, demonstrated that
children categorized by the algorithm as having primary care visits with attention to
13. 7
elevated BMI and/or obesity-related medical risk were more likely to have improvement
in weight status at follow-up visits [34]. Based on the results of these studies out of Texas
and the anticipated benefits of using electronic phenotypes to augment provider practices,
we sought to replicate and externally validate the Turer algorithm[33] in the Yale New
Haven pediatric primary care setting.
14. 8
STATEMENT OF PURPOSE
Hypothesis: We anticipate that the algorithm developed by Turer et. al 2018 to identify
attention to elevated BMI and weight-related comorbidities among pediatric primary care
clinicians would be applicable to 6-12 year-old children with overweight/obesity, defined
as a BMI ≥85th
percentile, seen for well child visits in Yale New Haven Health pediatric
primary care practices. Specifically, we hypothesize that implementation of this
algorithm among patients at Yale-affiliated practices will yield a sensitivity and
specificity for predicting attention (per manual review of EHR documentation) that are
similar to the Turer study at a health system in Dallas, Texas. We also anticipate, based
on data from previous research, that children with obesity or severe obesity will be more
likely to be assigned an attention category in comparison to children with overweight,
and non-Hispanic Black and Hispanic children will be more likely to be assigned an
attention category than their non-Hispanic White counterparts [2, 25, 30, 35]. We expect
to see variations on clinical practice based on trainee level as has been documented
previously and therefore predict that children with encounters in the summer months
(July- September), when new physicians begin their residency training, will be more
likely to receive “no attention” than children seen later in other months [28, 36, 37].
Lastly, we predict that children with public versus private health care payors will be more
likely to receive higher levels of attention.
Specific Aim 1: To externally validate the algorithm described by Turer et. al 2018
among 6-12 year old children with overweight/obesity seen at the Yale New Haven
Hospital-affiliated pediatric primary care practices.
15. 9
Specific Aim 2: To examine associations between pediatric provider attention and 1)
weight category (overweight, obesity, severe obesity), 2) insurance type, 3) race/ethnicity
and 4) season of encounter.
16. 10
METHODS
Data Source:
We examined the records of 300 randomly selected patients ages 6-12 with two or
more measurements of elevated BMI percentiles (³85th
percentile) for age and sex who
were seen for well child visits on two or more occasions at any one of three pediatric
primary care practices in the Yale New Haven Health system: the Yale Pediatric Primary
Care Clinic (PCC), Yale Health Center (YHC), and Saint Raphael Campus Primary Care
Clinic (SRC) from June 1, 2018 to May 31, 2019. If multiple encounters for the same
patient occurred within that time period, we examined the encounter from the first
chronological date. We categorized children’s weight status based on the Center for
Disease Control (CDC) growth charts from 2000 which classifies BMI-for-age into the
following categories stratified by sex: overweight ³ 85th
to < 95th
percentile, and obesity
³95th
percentile[38]. The intention of this study was to only examine patients with
overweight or obesity.
Exclusion Criteria:
Patients were excluded from the study if they had less than two recorded BMI
measurements above the 85th
percentile to ensure that we were not examining visits with
aberrant or incorrect BMI recordings. We also excluded children that were taking
medications or had conditions that impact growth and nutrition (e.g., pregnancy, thyroid
dysfunction, growth hormone abnormalities and sex hormone abnormalities).
Measures and Data Collection:
We extracted the following variables from the medical records of eligible patients:
17. 11
a. Visit and problem list diagnosis codes entered on the date of the
encounter
b. Referrals entered on the date of the encounter
c. Procedures/ lab orders entered on the date of the encounter
d. Medication lists queried for prescriptions written on day of the encounter
e. Age calculated in months based off of patient’s birthdate and age at visit
f. BMI calculated using height and weight on the date of the visit
g. BMI categorization defined as overweight (≥85th
– < 95th
percentile),
obese (≥95th
- <120% of the 95th
percentile) and severely obese (>120% of the
95th
percentile) using CDC growth charts BMI for age.
h. Sex (Male or Female)
i. Race/ethnicity defined as non-Hispanic Black, non- Hispanic White,
Hispanic and Asian.
j. Insurance type defined as public (Medicaid), private (Blue Cross Blue
Shield, Managed, or other commercial insurance), and Uninsured (self-pay or
missing)
k. Provider type: defined as Nurse Practitioner, Physician Assistant,
Physician (Attendings and Fellows), and Resident
Construction and Implementation of Algorithm to Detect Provider Attention:
The algorithm was modeled after the electronic phenotype described by Turer et.
al[33]. After collecting the diagnostic codes, laboratory studies, medications, procedures
and referrals used by our cohort, patient visits were classified into the following broad
attention types: No Attention, Attention to BMI alone, Attention to Comorbidities alone,
18. 12
and Attention to BMI and Comorbidities. The cohort was then sub-classified into
comorbidity subtypes (Attention to Diabetes, Attention to Fatty Liver Disease, Attention
to Hyperlipidemia and Attention to Vitamin D Deficiency) based on criteria listed by
Turer et al (Figure 1). Criteria for classifying visits into attention types are listed in Table
1 for broad categories and Table 2 for comorbidity sub-types. The criteria were defined
by reviewing the diagnostic codes, laboratory studies, medications, referrals and
procedures used by Turer’s team and comparing them to the corresponding values used in
our population. Given that the original study was conducted using ICD-9 diagnostic
codes, we first converted all codes into ICD-10 using the following website:
http://www.icd10codesearch.com/.
19. 13
Table 1. Diagnostic codes, laboratory studies, referrals and procedures that were used to qualify for each attention type
based on the data from Yale pediatric primary care practices
Attention Type Diagnosis Code (ICD-10) Medicines and
Laboratory Studies
Referrals
and
Procedures
BMI alone
E66.09 Obesity due to excess calories, unspecified
obesity severity
E66.09, Z68.54 Obesity due to excess calories
without serious comorbidity with body mass index
(BMI) in 95th to 98th percentile for age in
pediatric patient
E66.3 Overweight
E66.3, Z68.53 Overweight peds (BMI 85-94.9
percentile)
E66.3, Z68.54 Body mass index (BMI) of 95th to
99th percentile for age in pediatric patient
E66.9 Obesity (BMI 30-39.9)/Obesity, unspecified
classification, unspecified obesity type,
unspecified whether serious comorbidity present
E66.9, Z68.53 Obesity, pediatric, BMI 85th to less
than 95th percentile for age
E66.9, Z68.54 Obesity peds (BMI >=95
percentile)/BMI (body mass index), pediatric 95-
99% for age, obese child structured weight
management/multidisciplinary intervention
category
R63.3 Feeding difficulties
R63.5 Weight gain/abnormal weight gain
Z68.41BMI 40.0-44.9, adult (HC Code)
Z68.53 BMI (body mass index), pediatric, 85% to
less than 95% for age
Z68.54 BMI pediatric, greater than or equal to
95% for age
Z71.3 Nutritional counseling
Z72.4 Inappropriate diet and eating habits
Orlistat
DNA methylation analysis for
Angelman or Prader Willi
Syndrome
Mutation analysis for Angelman or
Prader Willi Syndrome
*No patients in our population were
taking medicines to treat obesity
*No patients in our population had
lab studies for Angelman, Prader
Willi syndrome targeted gene
mutation analysis or DNA
methylation analysis DNA
methylation analysis
Nutrition
counseling
Exercise
counseling
Nutrition Referral
Attention to BMI and
Comorbidity
³1 diagnosis code for BMI and ³1 diagnosis code
for:
• Elevated blood pressure/hypertension
• Acanthosis, prediabetes, diabetes type 2
• Lipid disorders
• Fatty liver disease
• Vitamin D deficiency
*See table 2 for codes broken up by comorbidity
Labs to screen for diabetes, lipid
disorders, fatty liver disease, or
vitamin D deficiency (2 lab orders
required, because increased
likelihood lab ordered to screen
for comorbidities related
to overweight/obesity)
• Medicines to treat hypertension,
diabetes, lipid disorders, or
vitamin D deficiency
*See table 2 for laboratory studies
broken up by comorbidity
Referral to
tertiary weight
management
clinic, other
medical
specialist
(endocrinologist,
GI, Cardiologist,
etc)
*See table 2 for
referrals broken
up by
comorbidity
Attention to
Comorbidity Alone
³1 diagnosis code for:
• Elevated blood pressure/hypertension
• Acanthosis, prediabetes, diabetes type 2
• Lipid disorders
• Fatty liver disease
• Vitamin D deficiency
*See table 2 for codes broken up by comorbidity
Labs to screen for diabetes, lipid
disorders, fatty liver disease, or
vitamin D deficiency (2 lab orders
required, because increased
likelihood lab ordered to screen
for comorbidities related
to overweight/obesity)
• Medicines to treat hypertension,
diabetes, lipid disorders, or
vitamin D deficiency
*See table 2 for laboratory studies
broken up by comorbidity
Referral to
tertiary weight
management
clinic, other
medical
specialist
(endocrinologist,
GI, Cardiologist,
etc)
*See table 2 for
referrals broken
up by
comorbidity
No attention
None of the above None of the above None of the
above
20. 14
Figure 1: Overview of how attention types are assigned based on EHR collected variables. Please see Table 1 for
specific diagnostic codes, laboratory orders, medication prescriptions and referrals for each attention type.
Comorbidity
Attention Type
Diagnosis Codes Referrals Laboratory Studies Medicines
Attention to Hypertension
R03.0: Elevated BP without
diagnosis of hypertension/single
episode of elevated blood
pressure reading/ borderline
hypertension
Pediatric
Nephrology
*No lab orders were used in
original study for this
comorbidity
acetazolamide
amlodipine
atenolol
hydralazine
propranolol
Attention to Diabetes
E11.65: Uncontrolled type 2
diabetes mellitus without
complication, without long-term
current use of insulin
L83: Acanthosis Nigricans
R73.03: Attention to Diabetes
R73.09: Elevated hemoglobin
A1c
Z13.1: Diabetes mellitus
screening
Endocrinology,
Diabetes &
Metabolism
Glucose
Glucose, fasting
Glucose, gray top
HEMOGLOBINA1C
Insulin, total (BH GH LMW Q
YH)
Insulin, total (BH GH Q YH)
insulin aspar prot-
insulin aspart
insulin aspart
insulin degludec
insulin detemir
insulin glargine
insulin lispro
insulin lispro
protamine-lispro
metformin
metformin ER
Attention to
Hyperlipidemia
Z13.220: Screening for
cholesterol level
Pediatric
cardiology
Cardiovascular
Disease
Cholesterol, Total
HDL cholesterol
LDL CHOLESTEROL,
DIRECT
LIPID PANEL
LIPID PANEL WITH
LDL/HDL RATIO (L)
atorvastatin
21. 15
Lipid profile w/ non-HDL
cholesterol (L)
Attention to Fatty Liver
Disease
R74.0: Elevated ALT
measurement
R74.8: Elevated liver enzymes
R94.5: Elevated LFTs
Pediatric
Hepatology
ALT
ALT+AST
AST
Hepatic function panel
Hepatic function panel (LFT)
LIVER FUNCTION TESTS
(YH)
Comprehensive metabolic panel
Comprehensive metabolic panel
without glucose
Attention to Vitamin D
Deficiency
E55.9: Vitamin D deficiency QuestAssureD 25-OH vitamin
D, (D2,D3), LC/MS/MS (LMW
Q)
QuestAssureD 25-OH vitamin
D, (D2,D3), LC/MS/MS (BH
LMW Q)
VITAMIN D, 25-HYDROXY,
TOTAL (GH L)
Vitamin D 25 hydroxy (BH
LMW)
Vitamin D, 25-hydroxy,
LC/MS/MS (Q YH)
cholecalciferol
(vitamin D3)
ergocalciferol
Table 2: Values used to qualify for attention to each comorbidity subtype based on data from our Yale associated
practices.
Reference Standard:
To evaluate the performance of the electronic phenotype, an independent chart
review process was done to validate the algorithm’s ability to detect “No Attention”. The
dual purpose of the review was to manually examine the values used by the algorithm
(diagnostic codes, labs, etc.) and to inquiry the visit encounter for other evidence of
attention for which the algorithm was not designed to detect such as written text, or
media entries. This process was done with substantial input from Dr. Christy Turer and
attempts were made to maintain fidelity between the chart review she conducted and
ours.
We began by examining the chart review/abstraction guide and questionnaire used
in the Turer et al 2018 study to review 300 charts. We first converted her questionnaire
from a paper copy to an online version in Qualtrics™ survey software (Qualtrics, Provo,
UT). Several questions in the original questionnaire were intended to collect data for
22. 16
other projects and were thus eliminated from our survey. Given that there are institutional
differences in EPIC layout and note templates, our chart review guide had to be adapted
to give clear directions for how to locate the desired information. In conjunction with Dr.
Turer and in effort to replicate her team’s process as much as possible, we also modified
and added questions to the chart abstraction questionnaire to improve clarity and
completeness. Examples of this include looking for laboratory orders in addition to
laboratory results and adding a question to manually look at visit and problem list
diagnostic codes associated with an encounter. Chart reviews included reviewing the
growth chart, problem list, medication list, laboratory studies, family history, externally
uploaded media, and visit notes associated with the visit date for each patient. Each chart
was reviewed systematically using the Qualtrics survey. We used two separate reviewers
(AG, AF) to examine 30 charts (10% of total) and compared responses to each Qualtrics
survey question. Discrepancies in the responses were resolved with either a third-party
reviewer (MS) or direct discussion between the reviewers. Interrater reliability was
measured using the kappa statistic and interpreted using the guidelines outlined by Koch
and Landis[39].
A difference in our chart review process in comparison to the original project was
the selection of charts. Dr. Turer’s team randomly selected 100 charts from each attention
type (No Attention, Attention to BMI, Attention to Comorbidity). To control for bias, we
chose to blindly review 300 charts from the cohort of 6-12 year olds and compare
assigned attention types after completion of the review.
Statistical Analysis:
Our primary outcome was the algorithm’s sensitivity and specificity of predicting
no attention versus any attention compared to the reference standard.
23. 17
The secondary analysis looked at demographic differences between attention
types (no attention, attention to BMI alone, attention to Comorbidities alone, and
attention to BMI and Comorbidities), for both chart review and algorithm, using Chi-
squared tests of association.
Kappa statistics were computed by hand for interrater reliability (n = 2) of the
chart review. All other analyses was completed using SAS version 9.4 (SAS Institute,
Cary, NC).
Responsibilities:
The thesis primary author (AGB) was responsible for IRB writing and approval
(with oversight from Dr. Sharifi), data randomization, creation of the chart review tool
and review of clinic encounters. Kaitlin Maciejewski, MS (biostatistician) developed the
SAS code to implement the algorithm and to conduct the statistical analysis. Additional
support clarifying which variables were included in the original algorithm in Texas and
general guidance was provided by Christy Boling Turer MD, MHS. Ada Fenick, MD
assisted with the duplicate review of 10% of clinical encounters and helped refine the
chart review tool together with Drs. Turer and Sharifi.
24. 18
RESULTS
Demographics:
We reviewed 329 charts to identify 300 encounters that met our inclusion criteria
and excluded 29 charts due to BMI measurements not meeting inclusion criteria. The
mean±SD age of the sample was 10±1.87 years and 58.3% of children were male. Table
3 displays the demographics and encounter characteristics of the sample. In terms of
weight categorization, 15.3% met criteria for severe obesity defined as ≥120% of 95th
percentile, 41.3% met criteria for obesity, and 43.3% met criteria for overweight. The
most prevalent race/ethnicity was Hispanic/Latino, compromising 42.7% of the cohort,
Non-Hispanic Black was the next most prevalent 31% followed by Non-Hispanic White
(15%) and Asian/Other (11.3%). Of the clinics included in our cohort, the majority (63.7
%) of encounters were conducted at the PCC, 24.7% at YHC, and 11.7% at SRC. The
majority of patients in our sample had a public insurance payor type (64.7%) and the
remainder had a private payor (14.3%) such as a managed healthcare or “Blue-cross
Blue-shield,” or other means (20.7%). Of the visit encounters examined, 49% were
conducted by resident physicians with attending supervision, 25% by nurse Practitioners,
17.3% by attendings and 8.3% by physician assistants.
Table 5 displays the prevalence of attention to BMI and/or obesity-related
comorbidities stratified by demographic and encounter characteristics. We observed
statistically significant differences in the likelihood of classification as “no attention” by
BMI category (p<0.001 for chart review and p<0.001 for algorithm) and by clinician type
(p<0.001 for chart review and p<0.001 for algorithm). We did not observe statistically
significant differences by race/ethnicity, season of encounter or insurance type.
Validation of the Algorithm:
25. 19
Of the 30 charts comprehensively reviewed by two reviewers (AG and AF),
kappa scores suggested substantial inter-observer agreement: 0.697 (95% confidence
interval 0.482-0.912). Of the 300 charts reviewed in total, 50 were assigned no attention
by chart review compared to 99 assigned no attention by the algorithm. Chart review
identified 102 charts as attention to BMI, 4 as attention to comorbidity alone, and 144 as
attention to BMI and Comorbidity. The algorithm correctly identified 66 and 82 as
Attention to BMI and Attention to BMI and Comorbidity, respectively. This yielded a
sensitivity of 94.0%, specificity of 79.2%, positive predictive value 47.5%, and negative
predictive value 98.5% (Table 4).
A review of the charts for which the algorithm incorrectly labeled the encounter
as “no attention,” the discordance between the algorithm and chart review was due to
evidence of attention in the form of free text within the progress note. Of the 52
encounters incorrectly labeled as no attention” by the algorithm, 83% had evidence of
documentation in the assessment and plan and 49% had evidence of attention in the
subjective sections of the progress note. A few encounters were incorrectly labeled as
attention by the algorithm due to the use of qualifying laboratory studies, diagnosis
codes, or referrals but not in the context of weight management. For example, in one
encounter the provider ordered screening lipids as part of routine care during a 10 year-
old’s well child visit in addition to vitamin D screening as part of deficiency screening in
refugee clinic; these two lab orders satisfy the algorithm’s categorization of “attention to
comorbidity” but were appropriately not identified as attention during chart review.
These findings are summarized in Figure 2.
26. 20
Characteristic
Overall Sample
N (%)
BMI categorization
Overweight 130 (43.3%)
Obesity (Class 1 only) 124 (41.3%)
Severe Obesity (Class 2 and 3) 046 (15.3%)
Race/ethnicity
Asian/Other 033 (11.0%)
Hispanic or Latino 128 (42.7%)
Non-Hispanic Black 094 (31.3%)
Non-Hispanic White 045 (15.0%)
Provider type
Attending 053 (17.7%)
Nurse Practitioner 075 (25.0%)
Physician Assistant 025 (08.3%)
Resident 147 (49.0%)
Season
Fall 072 (24.0%)
Spring 062 (20.7%)
Summer 106 (35.3%)
Winter 060 (20.0%)
Insurance type
Private Health care 044 (14.7%)
Public health care 194 (64.7%)
Other / missing 062 (20.7%)
Clinic
SRC 035 (11.7%)
YHC (pediatrics) 074 (24.7%)
YNH-PCC 191 (63.7%)
Table 3 Demographics of the sample of 300 encounters examined. Obesity class 1 was defined as BMI percentile ³ 95th
to <120%BMIP95. Obesity Class 2 and 3 was defined as BMI percentile ≥120 %BMIp95.
Chart review attention
type
Algorithm flag
Attention to
BMI
Attention to
BMI
and
Comorbidity
Comorbiditi
es only
No
attention
Total
Attention to BMI 66 2 0 34 102
Attention to BMI
and Comorbidity 27 82 18 17 144
Comorbidities only 0 0 3 1 4
No attention 0 0 3 47 50
Total 93 84 24 99 300
Table 4: Comparison of algorithm and chart review attention assignments
27. 21
Table 5: Chi-squared test of association between receiving provider attention and relevant clinical and demographic
variables
Characteristic
Chart-Review Defined Algorithm Defined
Attention to
BMI or BMI &
comorbidity or
Comorbidity
alone
No
Attention
P
value*
Attention to
BMI or BMI &
comorbidity or
Comorbidity
alone
No
Attention
P
value
BMI categorization
Overweight 095 (38.0%) 035 (70.0%)
<0.001
070 (34.8%) 060 (60.6%)
<0.001
Obesity (class
1 only)
110 (44.0%) 014 (28.0%) 093 (46.3%) 031 (31.3%)
Severe
Obesity (class
2 and 3)
045 (18.0%) 001 (02.0%) 038 (18.9%) 008 (08.1%)
Race/ethnicity
Asian/Other 030 (12.0%) 003 (06.0%)
0.19
025 (12.4%) 008 (08.1%)
0.47
Hispanic or
Latino
108 (43.2%) 020 (40.0%) 088 (43.8%) 040 (40.4%)
Non-Hispanic
Black
079 (31.6%) 015 (30.0%) 058 (28.9%) 036 (36.4%)
Non-Hispanic
White
033 (13.2%) 012 (24.0%) 030 (14.9%) 015 (15.2%)
Provider type
Attending 050 (20.0%) 003 (06.0%)
<0.001
040 (19.9%) 013 (13.1%)
<0.001
Nurse
Practitioner
052 (20.8%) 023 (46.0%) 040 (19.9%) 035 (35.4%)
Physician
Assistant
008 (03.2%) 017 (34.0%) 005 (02.5%) 020 (20.2%)
Resident 140 (56.0%) 007 (14.0%) 116 (57.7%) 031 (31.3%)
Season
Fall 061 (24.4%) 011 (22.0%)
0.75
046 (22.9%) 026 (26.3%)
0.83
Spring 054 (21.6%) 008 (16.0%) 040 (19.9%) 022 (22.2%)
Summer 086 (34.4%) 020 (40.0%) 073 (36.3%) 033 (33.3%)
Winter 049 (19.6%) 011 (22.0%) 042 (20.9%) 018 (18.2%)
Insurance type
Private Health
care
038 (15.2%) 006 (12.0%)
0.68
030 (14.9%) 014 (14.1%)
0.06
Public health
care
159 (63.6%) 035 (70.0%) 122 (60.7%) 072 (72.7%)
Other /
missing
053 (21.2%) 009 (18.0%) 049 (24.4%) 013 (13.1%)
Clinic
SRC 016 (06.4%) 019 (38.0%)
<0.001
012 (06.0%) 023 (23.2%)
<0.001
YHC
(pediatrics)
063 (25.2%) 011 (22.0%) 053 (26.4%) 021 (21.2%)
YNH-PCC 171 (68.4%) 020 (40.0%) 136 (67.7%) 055 (55.6%)
28. 22
Figure 2: Summary of discrepancies between algorithm flags and chart review flags.
29. 23
DISCUSSION
The purpose of this project was to assess the external validity of an electronic
phenotyping algorithm that evaluates clinician attention to elevated BMI and associated
comorbidities during pediatric primary care visits against a reference standard of manual
chart review. Our chart review revealed that 250 out of 300 encounters (83%) had some
evidence of attention to weight status or weight-related comorbidities in the electronic
record associated with the visit, and 50 of the 300 (16%) encounters examined lacked
evidence of any attention. The electronic phenotyping algorithm flagged 201 out of 300
(67%) visits with an attention type (attention to BMI, attention to comorbidity or
attention to BMI and comorbidity) and 99 out of 300 (33%) visits with no evidence of
attention. The application of the Turer algorithm to the Yale pediatric primary care
setting had a sensitivity of 94.0% and a specificity of 79.2% for predicting “no attention”
versus any attention type. The positive predictive value of “no attention” relative to any
attention type in our cohort was 47.5% and the negative predictive value of “no attention”
was 98.5%. When examining the encounters labeled as “no attention”, we found
significant differences in assignment based on weight category, consistent with previous
research [2, 25, 30], as well as by clinician type.
In comparison with the performance of the phenotyping algorithm in the original
Turer et al study population, our algorithm had a slightly lower sensitivity and
substantially lower specificity for identifying “no attention”. Using the sensitivity and
specificity from the Turer et. al, study we would expect a positive predictive value of
79.0% in our sample. However, our minimally modified algorithm yielded a positive
predictive value of 47.5%. The suboptimal specificity and positive predictive value could
30. 24
be explained by a lack of generalizability of the original algorithm. The algorithm
developed by Turer et al was refined based on the practices of documentation conducted
at the UT Southwestern clinic sites. Several iterations of code were reviewed to enhance
the performance of the algorithm in their practices. However, documentation practices
regarding the structured inputs that the algorithm evaluated such as problem list entry or
ICD 10 codes vary between sites. An example of this could be seen in the ordering of
Vitamin D labs and the reporting of Vitamin D Deficiency. Vitamin D deficiency has
been known to be associated with overweight/obesity and was included in the Turer
algorithm as an indicator of attention to weight status. However, Vitamin D deficiency is
routinely evaluated in the relatively large population of refugee patients seen at the Yale
PCC, regardless of weight status, causing the algorithm to incorrectly identify attention to
a comorbidity (false positives) and limiting the generalizability of the algorithm to the
Yale primary care setting.
Although our electronic phenotype had a lower sensitivity and specificity than the
original study, its use of multiple EHR inputs make it a better predictor of attention than
ICD diagnosis codes alone. For example, compared to a study completed at Yale
pediatric primary care sites between November 2011 and May 2015, only 11% of
children with overweight had a visit diagnoses of overweight in their medical record,
37% of children with obesity has a diagnosis code, and 54% of children with severe
obesity received had the corresponding diagnosis code [35]. Other studies examining the
use of diagnosis codes for overweight and obesity, have shown similarly low rates[2, 40,
41]. This suggests that while diagnosis of obesity or overweight may still be sub-optimal,
clinicians may be using other areas of the EHR to denote attention to weight status and
31. 25
weight related comorbidities in their patients. Evaluating provider behavior solely on
diagnosis codes provides an incomplete assessment of the clinic encounter.
Although a substantial improvement over diagnosis codes alone, the Turer
algorithm is limited to structured data and does not utilize the large amount of
unstructured data available in EHR encounter, i.e, free text in clinical notes. It has already
been established that clinicians have suboptimal rates of entering diagnosis codes and
problem list entries. However, the clinical progress note is an important medical-legal
document that many providers rely on to communicate information. We found that of the
encounters that were incorrectly labeled as no attention” by the algorithm, a large
percentage of them (83%) had evidence of documentation in the assessment and plan
and/or in the subjective sections (49%) of the progress note. By excluding free-text
elements in the clinical progress note from our evaluation of a clinical encounter, we are
capturing an incomplete picture of the encounter. Future studies looking to continue use
of electronic phenotyping to assess provider behavior should include natural language
processing to capture unstructured EHR inputs.
Another limitation of this study is that it primarily evaluated the algorithm in
mostly academic primary care settings. Although one of our study sites was a non-
teaching clinical environment, it is still difficult to predict performance in different
settings such as private practices.
In conclusion, our findings suggest that implementation of an electronic
phenotyping algorithm without adaption for the local site may result in lower specificity
than originally reported out of the health system in which the algorithm was developed.
Although still clinically useful and superior to other available options, adaptions such as
32. 26
the use of natural language processing could enhance the precision and accuracy of this
phenotyping algorithm as a pragmatic tool to detect attention to elevated BMI and
associated comorbidities.
33. 27
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