An introduction to machine learning for social scientists and epidemiologists, focusing on three ways these approaches may improve inference in these fields
Formation of Aspirations –An Empirical Analysisessp2
This document summarizes a study on the formation of aspirations using empirical data from Ethiopia. The main findings are:
1) Poorer individuals and those in poorer communities tend to have lower wealth aspirations.
2) Women appear to have lower aspirations than men.
3) Individuals with an internal locus of control (a belief they can control their lives) have higher aspirations and achieve better outcomes like education levels and farming practices.
The study finds correlations between various socioeconomic factors and aspiration levels using panel data from Ethiopia, with implications for designing poverty programs to encourage opportunity creation.
1. The document discusses performing a financial valuation and sensitivity analysis of Qantas Airline, which is listed on the Australian Stock Exchange.
2. It involves constructing a characteristic line to determine Qantas' beta, which measures the volatility of its returns relative to the market. The beta indicates whether it is an equity or asset beta.
3. Historical financial statement data for Qantas from the past 5 years is rebuilt to extract relevant cash flow information needed for the net present value analysis.
4. Financial forecasts are made for Qantas for another 5 years, explaining the method used to derive the forecast data. A sensitivity analysis
EPI_-_EPIDEMIOLOGIC_MEASURES in public health .pptxTofikMohammadMuse
This document discusses measures used to describe health events in populations, including ratios, proportions, and rates. It defines key terms like ratio, proportion, rate, incidence proportion, incidence density, point prevalence, and period prevalence. Examples are provided to demonstrate how to calculate various measures from population data, including the ratio of males to females, proportion of infants who lived, and incidence and prevalence rates. Mortality measures like crude mortality rate and category-specific rates are also defined.
Population projection (30 1-2017) by dr min ko koMmedsc Hahm
This document provides information on population estimation and projection methods. It begins by stating the expected learning outcomes and defining the key differences between estimation, which focuses on the present or recent past population, and projection, which focuses on future populations. It then discusses purposes of population projection like government planning. The document outlines several methods for estimation and projection including mathematical, component, and cohort component methods. It provides details on assumptions for fertility, mortality, and migration used in projections. It also discusses presenting projection outputs and revising projections over time.
Understanding and interpreting data is the process by which we make sense of data and such a process involves various ways of looking at the data. In this workshop, we will examine one of the most prevalent ways of looking at injury data, using examples from injury mortality and hospitalization data in British Columbia.
This document discusses the pros and cons of multinational corporations (MNCs) operating in the Chinese market through joint ventures with Chinese companies. It notes that MNCs can benefit from China's large market and low production costs. However, challenges include rising labor costs, intellectual property risks, and complying with China's laws and bureaucracy. Joint ventures help MNCs navigate these issues but come with governance complexities. Overall, China remains an important market for MNCs despite the challenges.
Homework #1SOCY 3115Spring 20Read the Syllabus and FAQ on ho.docxpooleavelina
Homework #1
SOCY 3115
Spring 20
Read the Syllabus and FAQ on how to do your homework before beginning the assignment!
To get consideration for full credit, you must:
· Follow directions;
· Show all work required to arrive at answer (statistical calculations often require multiple steps, so you need to write these down, not just skip to the final answer)
· Use appropriate statistical notation at all times (e.g. if you are calculating a population mean, begin with the equation for population mean)
· Use units in your answer, where appropriate (e.g. a mean time would be “6.5 hours” rather than just “6.5”)
Understanding the Structure of Data
1. For the following rectangular dataset:
Id
Highest degree
Works full-time
Annual income cat
1
Did not grad HS
Yes
Low
2
HS dip
Yes
Low
3
HS dip
No
Med
4
BA
No
Low
5
BA
Yes
Med
6
MA
Yes
High
7
HS dip
Yes
Med
a. What is the unit-of-analysis of the dataset?
b. How many variables are in the dataset?
c. How many observations/cases are in the dataset?
d. For eachvariable that is not named “id”:
i. What is the variable name?
ii. What is the level-of-measurement?
iii. What are the values for the variable?
iv. If you had to make a guess, what do you think the “question” was that was asked of the unit-of-analysis to get these data? (for example, if we had a continuous variable called “num_pets” the question might be “How many pets live in your household?”)
2. For the following rectangular dataset:
Id
num_bdrms
num_bthrms
sqft
Ranch
1
4
3
3200
Yes
2
2
1.5
2800
Yes
3
2
1
1200
Yes
4
3
2
1500
No
5
2
2
1100
No
a. What is the unit-of-analysis of the dataset?
b. How many variables are in the dataset?
c. How many observations/cases are in the dataset?
d. For each variable that is not named “id”:
i. What is the variable name?
ii. What is the level-of-measurement? Before answering, be sure to consult the slide called “Level of measurement – language to use”. Use the formal language!
iii. What are the values for the variable?
iv. If you had to make a guess, what do you think the “question” was that was asked of the unit-of-analysis to get these data? (for example, if we had a continuous variable called “num_pets” the question might be “How many pets live in your household?”)
3. For each of the following questions (1) construct a dataset with one variable and three observations (2) add data that could have theoretically been collected (just make up the actual responses to the question); and (3) indicate the level-of-measurement of the variable. I’ve done two examples for you.
Example#1:
What is your current age? (individual is the unit-of-analysis)
idage
1 25
2 32
3 61
The age variable is continuous/interval ratio.
Example#2:
What is the size of this hospital based on number of beds? (hospital is the unit-of-analysis)? Answers can be small (1-100 beds), medium (101-500 beds), large (501 beds to 1000 beds), extra large (1001+ beds)
idhosp_size
1 med
2 med
3 ext ...
Harnessing Data to Improve Health Equity - Dr. Ali MokdadLauren Johnson
1) The document discusses methods used by the Institute for Health Metrics and Evaluation (IHME) to conduct comprehensive analyses of global, national, and subnational disease burden through their Global Burden of Disease (GBD) study.
2) Key methods discussed include garbage code redistribution to reassign unspecified causes of death, Bayesian meta-regression to estimate incidence and prevalence, and small area statistical models that borrow strength across space, time, and covariates to produce estimates of disease burden for locations with limited data.
3) The GBD study aims to quantify health loss from major diseases, injuries, and risk factors globally and over time in order to help identify and address the world's most pressing health challenges.
Formation of Aspirations –An Empirical Analysisessp2
This document summarizes a study on the formation of aspirations using empirical data from Ethiopia. The main findings are:
1) Poorer individuals and those in poorer communities tend to have lower wealth aspirations.
2) Women appear to have lower aspirations than men.
3) Individuals with an internal locus of control (a belief they can control their lives) have higher aspirations and achieve better outcomes like education levels and farming practices.
The study finds correlations between various socioeconomic factors and aspiration levels using panel data from Ethiopia, with implications for designing poverty programs to encourage opportunity creation.
1. The document discusses performing a financial valuation and sensitivity analysis of Qantas Airline, which is listed on the Australian Stock Exchange.
2. It involves constructing a characteristic line to determine Qantas' beta, which measures the volatility of its returns relative to the market. The beta indicates whether it is an equity or asset beta.
3. Historical financial statement data for Qantas from the past 5 years is rebuilt to extract relevant cash flow information needed for the net present value analysis.
4. Financial forecasts are made for Qantas for another 5 years, explaining the method used to derive the forecast data. A sensitivity analysis
EPI_-_EPIDEMIOLOGIC_MEASURES in public health .pptxTofikMohammadMuse
This document discusses measures used to describe health events in populations, including ratios, proportions, and rates. It defines key terms like ratio, proportion, rate, incidence proportion, incidence density, point prevalence, and period prevalence. Examples are provided to demonstrate how to calculate various measures from population data, including the ratio of males to females, proportion of infants who lived, and incidence and prevalence rates. Mortality measures like crude mortality rate and category-specific rates are also defined.
Population projection (30 1-2017) by dr min ko koMmedsc Hahm
This document provides information on population estimation and projection methods. It begins by stating the expected learning outcomes and defining the key differences between estimation, which focuses on the present or recent past population, and projection, which focuses on future populations. It then discusses purposes of population projection like government planning. The document outlines several methods for estimation and projection including mathematical, component, and cohort component methods. It provides details on assumptions for fertility, mortality, and migration used in projections. It also discusses presenting projection outputs and revising projections over time.
Understanding and interpreting data is the process by which we make sense of data and such a process involves various ways of looking at the data. In this workshop, we will examine one of the most prevalent ways of looking at injury data, using examples from injury mortality and hospitalization data in British Columbia.
This document discusses the pros and cons of multinational corporations (MNCs) operating in the Chinese market through joint ventures with Chinese companies. It notes that MNCs can benefit from China's large market and low production costs. However, challenges include rising labor costs, intellectual property risks, and complying with China's laws and bureaucracy. Joint ventures help MNCs navigate these issues but come with governance complexities. Overall, China remains an important market for MNCs despite the challenges.
Homework #1SOCY 3115Spring 20Read the Syllabus and FAQ on ho.docxpooleavelina
Homework #1
SOCY 3115
Spring 20
Read the Syllabus and FAQ on how to do your homework before beginning the assignment!
To get consideration for full credit, you must:
· Follow directions;
· Show all work required to arrive at answer (statistical calculations often require multiple steps, so you need to write these down, not just skip to the final answer)
· Use appropriate statistical notation at all times (e.g. if you are calculating a population mean, begin with the equation for population mean)
· Use units in your answer, where appropriate (e.g. a mean time would be “6.5 hours” rather than just “6.5”)
Understanding the Structure of Data
1. For the following rectangular dataset:
Id
Highest degree
Works full-time
Annual income cat
1
Did not grad HS
Yes
Low
2
HS dip
Yes
Low
3
HS dip
No
Med
4
BA
No
Low
5
BA
Yes
Med
6
MA
Yes
High
7
HS dip
Yes
Med
a. What is the unit-of-analysis of the dataset?
b. How many variables are in the dataset?
c. How many observations/cases are in the dataset?
d. For eachvariable that is not named “id”:
i. What is the variable name?
ii. What is the level-of-measurement?
iii. What are the values for the variable?
iv. If you had to make a guess, what do you think the “question” was that was asked of the unit-of-analysis to get these data? (for example, if we had a continuous variable called “num_pets” the question might be “How many pets live in your household?”)
2. For the following rectangular dataset:
Id
num_bdrms
num_bthrms
sqft
Ranch
1
4
3
3200
Yes
2
2
1.5
2800
Yes
3
2
1
1200
Yes
4
3
2
1500
No
5
2
2
1100
No
a. What is the unit-of-analysis of the dataset?
b. How many variables are in the dataset?
c. How many observations/cases are in the dataset?
d. For each variable that is not named “id”:
i. What is the variable name?
ii. What is the level-of-measurement? Before answering, be sure to consult the slide called “Level of measurement – language to use”. Use the formal language!
iii. What are the values for the variable?
iv. If you had to make a guess, what do you think the “question” was that was asked of the unit-of-analysis to get these data? (for example, if we had a continuous variable called “num_pets” the question might be “How many pets live in your household?”)
3. For each of the following questions (1) construct a dataset with one variable and three observations (2) add data that could have theoretically been collected (just make up the actual responses to the question); and (3) indicate the level-of-measurement of the variable. I’ve done two examples for you.
Example#1:
What is your current age? (individual is the unit-of-analysis)
idage
1 25
2 32
3 61
The age variable is continuous/interval ratio.
Example#2:
What is the size of this hospital based on number of beds? (hospital is the unit-of-analysis)? Answers can be small (1-100 beds), medium (101-500 beds), large (501 beds to 1000 beds), extra large (1001+ beds)
idhosp_size
1 med
2 med
3 ext ...
Harnessing Data to Improve Health Equity - Dr. Ali MokdadLauren Johnson
1) The document discusses methods used by the Institute for Health Metrics and Evaluation (IHME) to conduct comprehensive analyses of global, national, and subnational disease burden through their Global Burden of Disease (GBD) study.
2) Key methods discussed include garbage code redistribution to reassign unspecified causes of death, Bayesian meta-regression to estimate incidence and prevalence, and small area statistical models that borrow strength across space, time, and covariates to produce estimates of disease burden for locations with limited data.
3) The GBD study aims to quantify health loss from major diseases, injuries, and risk factors globally and over time in order to help identify and address the world's most pressing health challenges.
The document discusses risk communication in clinical practice. It defines risk communication as informing patients about risks to help them understand consequences and options. Effective risk communication is important for informed consent and shared decision making. However, communicating risk is challenging due to issues like health literacy, framing effects, and balancing population and individual perspectives. Studies show patients prefer absolute risks over relative risks and find some metrics like number needed to treat difficult. Risk scales and decision tools can help but may not address all challenges of risk communication in practice.
Chemical Risk Assessment and Translation to Socio-Economic AssessmentsOECD Environment
OECD Workshop on SocioEconomic Impact Assessment of Chemicals Management, Helsinki, 7 July 2016
Background paper 1: Chemical Risk Assessment and Translation to Socio-Economic Assessments, by Weihsueh A. Chiu, Texas A&M University
Risky Business: Risk communicat ion in the provider-patient encounterZackary Berger
Communicating risk is part of nearly every patient-provider encounter. I present some evidence-based strategies to improve patients\' and doctors\' risk perception.
Prevalence of Physical Activity and Barriers to Physical Activity Among Yerev...CRRC-Armenia
The document summarizes a study on physical activity prevalence and barriers to physical activity among adults in Yerevan, Armenia. Key findings include:
- 53% of the sample was inactive or minimally active, while 47% met health-enhancing physical activity levels.
- Occupation and marital status were significantly associated with physical activity levels.
- Most respondents agreed that physical activity is beneficial for health and known to prevent cardiovascular diseases.
Scenario You are a lieutenant in charge of an undercove.docxkenjordan97598
Scenario:
You are a lieutenant in charge of an undercover strike force team, charged with the responsibility of apprehending fugitives from justice. Your team has been criticized by the local media for some of its members' actions in carrying out their responsibilities, such as using questionable methods that could be seen as potential violation of some individual civil rights. Your team has been very effective in carrying out its assigned duties, resulting in an 80% apprehension rate.
You have been advised by the chief that all he wants is results, not excuses. He wants you to use whatever means are necessary to apprehend fugitives because anything less would reflect badly on the department and his leadership. He reminds you that he has the firm backing of the mayor and city commission in how he runs the department.
The next day, a news reporter informs you that he is working on a story regarding the apprehension of child rapist. Information he has gathered indicates that the arresting officers on the team, under your supervision, may have used questionable methods during the apprehension, which resulted in significant injuries to the individual. He asks for you to comment on the potential violation, and you inform him that you will look into the matter and get back with him later.
Later that evening, you call a meeting of your team and advise the members of the allegations made. It is then brought to your attention that there was some force used in the apprehension that may have exceeded what was necessary. The next morning, you advise the chief of the inquiry by the media, and you tell him that based on your preliminary inquiry, there may be some validity to what the reporter told you. He reminds you of what he expects out of your team: results, not excuses.
Ethics and Police Administration
Respond to the given scenario in 500-600 words addressing the following 8 questions
Due March 5th
Primary Task Response: Write 500–600 words that respond to the following questions with your thoughts, ideas, and comments. Be substantive and clear, and use examples to reinforce your ideas:
1. What do you think are the legal issues involved in the scenario? Explain.
2. What do you think are the ethical issues involved in the scenario? Explain.
3. What are the possible consequences of not addressing these ethical issues? Explain.
4. Considering the directive given to you by your chief that he wants results and not excuses, what are some of the factors that you should take into consideration?
5. How would you respond to the follow-up questions from the reporter? Why?
6. What will most likely result from your responses, and how will you protect yourself and your career? Explain.
7. How significant is it to you that a superior officer is implying that you should make an unethical decision? Explain.
8. How did this affect what you would say to the reporter? Explain.
*Must have a minimum of 2 reliable references with websit.
The Economic Burden of Asbestos-Related Cancers in Canada Uyen Vu
What would be the saving to society if we did not have any new cases of cancer attributable to occupational asbestos exposures in a particular year? That is the key question behind an economic burden study by the Institute for Work & Health, with support of the Canadian Cancer Society.
Lecture at EPISEA 2010 conference gaps in stragegic information on MARPs 24…Dr Ajith Karawita
This document discusses gaps in strategic information on most-at-risk populations (MARPs) in Sri Lanka. There is a lack of population size estimates and mapping of MARPs at the district level. Data on MARPs is also inadequate for HIV surveillance and estimates. It is difficult to obtain probability samples for surveillance and studies of MARPs due to their mobile nature. There is also insufficient expertise for MARP studies given Sri Lanka's low-level HIV epidemic status.
Magellan Health’s Programmatic Suicide Deterrent System David Covington
This document provides information about Arizona's Programmatic Suicide Deterrent System Project, including:
1) The project aims to reduce suicide rates in Maricopa County by training behavioral health staff to better identify and intervene with at-risk individuals.
2) Screening tools and clinical protocols have been developed for adults, adolescents, and children to stratify suicide risk levels and determine appropriate interventions.
3) An initial pilot program saw over 4,800 screens administered with a 16% positive rate and no reported suicides, demonstrating the potential effectiveness of the new screening and intervention strategies.
Risk preferences among small farmers in Lesotho: evidence from laboratory exp...The Transfer Project
Noemi Pace's (FAO) presentation at the Symposium on Economic Experiments in Developing Countries in University of California, Berkeley on 30th May 2019.
Alan Krupnick (Resources for the Future)'s keynote presentation to the OECD workshop on the socioeconomic impact assessment of chemicals management. Helsinki, 6 July 2016.
Benefits Of Exercise Essay. Benefits of Exercise Essay Essay on Benefits of ...Liza Shirar
Benefits of Exercise Essay Essay on Benefits of Exercise for Students .... Benefit Of Doing Exercise Essay Benefits Of Physical Exercise For Health. Write an essay on Importance of Physical Exercise Essay Writing .... Essay On Benefits Of Exercise Telegraph. 007 Benefits Of Regular Exercise Essay Thatsnotus. Essay on Exercise Benefits. Benefits of doing exercise essay. Benefits Of Exercise Essays .... Fitness Essay Short and Long Essay on Fitness Importance, Benefits .... Write short essay on Importance of Exercise Essay Writing English .... Benefits of doing exercise essay. Benefits of Doing Physical Exercise .... Benefits Of Exercise Essay / Benefits of Exercise Essay Essay on .... Benefit Of Doing Exercise Essay Benefits of Exercise Essay. Impressive Essay On Regular Exercise Thatsnotus. Importance of keeping fit essay. Importance Of Healthy And Staying Fit .... Importance Of Exercise Essay Essay On Importance Of Exercise Step by .... importance of exercise. Benefit Of Doing Exercise Essay Benefits of Exercise Essay. ᐅ Essays On Benefits of Exercise Free Argumentative, Persuasive .... Importance of exercise essay. Importance of Exercise. 2019-01-20. The Potential Benefits of Exercise Essay Example Topics and Well .... Essay On Physical Exercise Benefits Of Physical Exercise Essay 500 .... Essay on the importance of exercise in English. Exercise Is Good For Health Paragraph - Exercise Poster. Benefits of Exercise Research Paper Example Topics and Well Written .... Benefits of physical exercise essay. The Benefits of Physical .... Benefits of doing exercise essay. The Health Benefits of Exercise .... 019 Essay Example Fedhealth Exercise Infographic Jpg Benefits Thatsnotus. Importance of Exercise Essay 150 Words - Study-Phi. Exercise regularly essay. Exercise and Healthy Eating Should be the .... Importance Of Exercise For Students Essay - Exercise Poster Benefits Of Exercise Essay Benefits Of Exercise Essay. Benefits of Exercise Essay Essay on Benefits of Exercise for Students ...
Simcoe County - Infrastructure Table - RBA slide-deckMahendra Patel
This document provides an overview of Results-Based Accountability (RBA), which has two parts: performance accountability and population accountability. Performance accountability focuses on the well-being of populations served by programs, agencies, and service systems. Population accountability focuses on the well-being of whole populations at the community, city, and larger levels. The document outlines key RBA concepts like results, indicators, and performance measures. It also provides examples of applying RBA frameworks to measure outcomes in different domains like education, health, public safety, and the private sector.
This document provides information on sampling techniques. It defines sampling as using a subset of a larger population to make inferences about the whole population. The purpose of sampling is to provide statistical information about the whole population while examining only a selected portion to reduce costs and increase efficiency and reliability compared to a census. The document outlines the steps in the sampling process, including defining the population, identifying a sampling frame, determining the sample size, and selecting the sample. It provides an example of calculating sample size to estimate average weekly internet usage.
Sheet1Score -54321ScoreAccurately described the leader’s style, t.docxedgar6wallace88877
Sheet1Score ->54321ScoreAccurately described the leader’s style, traits and/or behaviors. Fully described. No additional improvement necessary. Mostly described. Only minimal improvement necessary. Moderately described. Improvement necessary. Minimally described. Room for significant improvement. Did not accurately describe. Applied course material to what you learned about the leader. Fully applied. No further Improvement necessary. Mostly applied. Only minimal improvement necessary. Moderately applied. Improvement necessary. Minimally applied. Room for significant improvement. Did not apply course material. Used citations from the week’s reading materials. Fully cited course materials. No further improvement necessary. Mostly cited course materials. Only minimal improvement necessary. Moderately cited course materials. Improvement necessary. Minimally cited. Room for significant improvement. Did not cite appropriately. Wrote with sufficient detail. Fully detailed. No further improvement necessary. Mostly detailed. Only minimal improvement necessary. Moderately detailed. Improvement necessary. Minimal detail. Room for significant improvement. Did not provide sufficient detail. Used appropriate grammar, punctuation and masters-level writing style Fully used appropriate writing style. No further improvements necessary. Mostly used appropriate writing style. Only minimal improvement necessary. Moderately used appropriate writing style. Improvement necessary. Minimally used appropriate writing style. Room for significant improvement. Did not use appropriate writing style. Final Score0
After reading Chapter 9 of Epidemiology for public health practice, complete Study Questions and Exercises 1–9. This activity is located on pages 431–432. Submit your responses in the form of a Word document.
1- Calculate the etiologic fraction when the RR for disease associated with a given exposure is 1.2, 1.8, 3, and 15.
2- The impact of an exposure on a population does not depend upon:
· a.the strength of the association between exposure and disease.
· b.the prevalence of the exposure.
· c.the case fatality rate.
· d.the overall incidence rate of disease in the population.
· The next seven questions (3–9) are based on the following data: The death rate per 100,000 for lung cancer is 7 among nonsmokers and 71 among smokers. The death rate per 100,000 for coronary thrombosis is 422 among nonsmokers and 599 among smokers. The prevalence of smoking in the population is 55%. (If necessary, refer to the chapter on cohort studies for formulas for RR.)
3- What is the RR of dying of lung cancer for smokers versus nonsmokers?
4-What is the RR of dying of coronary thrombosis for smokers versus nonsmokers?
5-What is the etiologic fraction of disease due to smoking among individuals with lung cancer?
6-What is the etiologic fraction of disease due to smoking among individuals with coronary thrombosis?
7-What is the population etiologic fraction of lung c.
This document provides an overview of a report on fertility trends in India. Secondary data sources to be used include Census 2011, Sample Registration System, National Family Health Surveys, and other population surveys. Primary data may also be collected through a student sample survey. Tools like Excel, Minitab, and SPSS will be used to analyze the data. The report will examine fertility rates across states and socioeconomic groups, and factors influencing the decline in fertility like increased education, media exposure, and family planning programs. Trends in late marriage and regional differences will also be analyzed. The goal is to understand drivers of infertility prevalence and dispel myths using data-backed insights.
Define epidemiology and identify the epidemiological models.pdfsdfghj21
This document discusses epidemiology and epidemiological models. It defines epidemiology as the study of the distribution and determinants of health and disease in populations. It identifies several epidemiological models including the person-place-time model, epidemiological triangle, wheel model of human-environment interaction, and web of causation. It also discusses descriptive and analytic epidemiology, different types of rates used to examine disease patterns (incidence rates, prevalence rates, mortality rates), and epidemiological methods like observational studies.
Kosmoderma Academy, a leading institution in the field of dermatology and aesthetics, offers comprehensive courses in cosmetology and trichology. Our specialized courses on PRP (Hair), DR+Growth Factor, GFC, and Qr678 are designed to equip practitioners with advanced skills and knowledge to excel in hair restoration and growth treatments.
The document discusses risk communication in clinical practice. It defines risk communication as informing patients about risks to help them understand consequences and options. Effective risk communication is important for informed consent and shared decision making. However, communicating risk is challenging due to issues like health literacy, framing effects, and balancing population and individual perspectives. Studies show patients prefer absolute risks over relative risks and find some metrics like number needed to treat difficult. Risk scales and decision tools can help but may not address all challenges of risk communication in practice.
Chemical Risk Assessment and Translation to Socio-Economic AssessmentsOECD Environment
OECD Workshop on SocioEconomic Impact Assessment of Chemicals Management, Helsinki, 7 July 2016
Background paper 1: Chemical Risk Assessment and Translation to Socio-Economic Assessments, by Weihsueh A. Chiu, Texas A&M University
Risky Business: Risk communicat ion in the provider-patient encounterZackary Berger
Communicating risk is part of nearly every patient-provider encounter. I present some evidence-based strategies to improve patients\' and doctors\' risk perception.
Prevalence of Physical Activity and Barriers to Physical Activity Among Yerev...CRRC-Armenia
The document summarizes a study on physical activity prevalence and barriers to physical activity among adults in Yerevan, Armenia. Key findings include:
- 53% of the sample was inactive or minimally active, while 47% met health-enhancing physical activity levels.
- Occupation and marital status were significantly associated with physical activity levels.
- Most respondents agreed that physical activity is beneficial for health and known to prevent cardiovascular diseases.
Scenario You are a lieutenant in charge of an undercove.docxkenjordan97598
Scenario:
You are a lieutenant in charge of an undercover strike force team, charged with the responsibility of apprehending fugitives from justice. Your team has been criticized by the local media for some of its members' actions in carrying out their responsibilities, such as using questionable methods that could be seen as potential violation of some individual civil rights. Your team has been very effective in carrying out its assigned duties, resulting in an 80% apprehension rate.
You have been advised by the chief that all he wants is results, not excuses. He wants you to use whatever means are necessary to apprehend fugitives because anything less would reflect badly on the department and his leadership. He reminds you that he has the firm backing of the mayor and city commission in how he runs the department.
The next day, a news reporter informs you that he is working on a story regarding the apprehension of child rapist. Information he has gathered indicates that the arresting officers on the team, under your supervision, may have used questionable methods during the apprehension, which resulted in significant injuries to the individual. He asks for you to comment on the potential violation, and you inform him that you will look into the matter and get back with him later.
Later that evening, you call a meeting of your team and advise the members of the allegations made. It is then brought to your attention that there was some force used in the apprehension that may have exceeded what was necessary. The next morning, you advise the chief of the inquiry by the media, and you tell him that based on your preliminary inquiry, there may be some validity to what the reporter told you. He reminds you of what he expects out of your team: results, not excuses.
Ethics and Police Administration
Respond to the given scenario in 500-600 words addressing the following 8 questions
Due March 5th
Primary Task Response: Write 500–600 words that respond to the following questions with your thoughts, ideas, and comments. Be substantive and clear, and use examples to reinforce your ideas:
1. What do you think are the legal issues involved in the scenario? Explain.
2. What do you think are the ethical issues involved in the scenario? Explain.
3. What are the possible consequences of not addressing these ethical issues? Explain.
4. Considering the directive given to you by your chief that he wants results and not excuses, what are some of the factors that you should take into consideration?
5. How would you respond to the follow-up questions from the reporter? Why?
6. What will most likely result from your responses, and how will you protect yourself and your career? Explain.
7. How significant is it to you that a superior officer is implying that you should make an unethical decision? Explain.
8. How did this affect what you would say to the reporter? Explain.
*Must have a minimum of 2 reliable references with websit.
The Economic Burden of Asbestos-Related Cancers in Canada Uyen Vu
What would be the saving to society if we did not have any new cases of cancer attributable to occupational asbestos exposures in a particular year? That is the key question behind an economic burden study by the Institute for Work & Health, with support of the Canadian Cancer Society.
Lecture at EPISEA 2010 conference gaps in stragegic information on MARPs 24…Dr Ajith Karawita
This document discusses gaps in strategic information on most-at-risk populations (MARPs) in Sri Lanka. There is a lack of population size estimates and mapping of MARPs at the district level. Data on MARPs is also inadequate for HIV surveillance and estimates. It is difficult to obtain probability samples for surveillance and studies of MARPs due to their mobile nature. There is also insufficient expertise for MARP studies given Sri Lanka's low-level HIV epidemic status.
Magellan Health’s Programmatic Suicide Deterrent System David Covington
This document provides information about Arizona's Programmatic Suicide Deterrent System Project, including:
1) The project aims to reduce suicide rates in Maricopa County by training behavioral health staff to better identify and intervene with at-risk individuals.
2) Screening tools and clinical protocols have been developed for adults, adolescents, and children to stratify suicide risk levels and determine appropriate interventions.
3) An initial pilot program saw over 4,800 screens administered with a 16% positive rate and no reported suicides, demonstrating the potential effectiveness of the new screening and intervention strategies.
Risk preferences among small farmers in Lesotho: evidence from laboratory exp...The Transfer Project
Noemi Pace's (FAO) presentation at the Symposium on Economic Experiments in Developing Countries in University of California, Berkeley on 30th May 2019.
Alan Krupnick (Resources for the Future)'s keynote presentation to the OECD workshop on the socioeconomic impact assessment of chemicals management. Helsinki, 6 July 2016.
Benefits Of Exercise Essay. Benefits of Exercise Essay Essay on Benefits of ...Liza Shirar
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Simcoe County - Infrastructure Table - RBA slide-deckMahendra Patel
This document provides an overview of Results-Based Accountability (RBA), which has two parts: performance accountability and population accountability. Performance accountability focuses on the well-being of populations served by programs, agencies, and service systems. Population accountability focuses on the well-being of whole populations at the community, city, and larger levels. The document outlines key RBA concepts like results, indicators, and performance measures. It also provides examples of applying RBA frameworks to measure outcomes in different domains like education, health, public safety, and the private sector.
This document provides information on sampling techniques. It defines sampling as using a subset of a larger population to make inferences about the whole population. The purpose of sampling is to provide statistical information about the whole population while examining only a selected portion to reduce costs and increase efficiency and reliability compared to a census. The document outlines the steps in the sampling process, including defining the population, identifying a sampling frame, determining the sample size, and selecting the sample. It provides an example of calculating sample size to estimate average weekly internet usage.
Sheet1Score -54321ScoreAccurately described the leader’s style, t.docxedgar6wallace88877
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After reading Chapter 9 of Epidemiology for public health practice, complete Study Questions and Exercises 1–9. This activity is located on pages 431–432. Submit your responses in the form of a Word document.
1- Calculate the etiologic fraction when the RR for disease associated with a given exposure is 1.2, 1.8, 3, and 15.
2- The impact of an exposure on a population does not depend upon:
· a.the strength of the association between exposure and disease.
· b.the prevalence of the exposure.
· c.the case fatality rate.
· d.the overall incidence rate of disease in the population.
· The next seven questions (3–9) are based on the following data: The death rate per 100,000 for lung cancer is 7 among nonsmokers and 71 among smokers. The death rate per 100,000 for coronary thrombosis is 422 among nonsmokers and 599 among smokers. The prevalence of smoking in the population is 55%. (If necessary, refer to the chapter on cohort studies for formulas for RR.)
3- What is the RR of dying of lung cancer for smokers versus nonsmokers?
4-What is the RR of dying of coronary thrombosis for smokers versus nonsmokers?
5-What is the etiologic fraction of disease due to smoking among individuals with lung cancer?
6-What is the etiologic fraction of disease due to smoking among individuals with coronary thrombosis?
7-What is the population etiologic fraction of lung c.
This document provides an overview of a report on fertility trends in India. Secondary data sources to be used include Census 2011, Sample Registration System, National Family Health Surveys, and other population surveys. Primary data may also be collected through a student sample survey. Tools like Excel, Minitab, and SPSS will be used to analyze the data. The report will examine fertility rates across states and socioeconomic groups, and factors influencing the decline in fertility like increased education, media exposure, and family planning programs. Trends in late marriage and regional differences will also be analyzed. The goal is to understand drivers of infertility prevalence and dispel myths using data-backed insights.
Define epidemiology and identify the epidemiological models.pdfsdfghj21
This document discusses epidemiology and epidemiological models. It defines epidemiology as the study of the distribution and determinants of health and disease in populations. It identifies several epidemiological models including the person-place-time model, epidemiological triangle, wheel model of human-environment interaction, and web of causation. It also discusses descriptive and analytic epidemiology, different types of rates used to examine disease patterns (incidence rates, prevalence rates, mortality rates), and epidemiological methods like observational studies.
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Promoting Wellbeing - Applied Social Psychology - Psychology SuperNotesPsychoTech Services
A proprietary approach developed by bringing together the best of learning theories from Psychology, design principles from the world of visualization, and pedagogical methods from over a decade of training experience, that enables you to: Learn better, faster!
Travel vaccination in Manchester offers comprehensive immunization services for individuals planning international trips. Expert healthcare providers administer vaccines tailored to your destination, ensuring you stay protected against various diseases. Conveniently located clinics and flexible appointment options make it easy to get the necessary shots before your journey. Stay healthy and travel with confidence by getting vaccinated in Manchester. Visit us: www.nxhealthcare.co.uk
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The UK is currently facing a Adhd Medication Shortage Uk, which has left many patients and their families grappling with uncertainty and frustration. ADHD, or Attention Deficit Hyperactivity Disorder, is a chronic condition that requires consistent medication to manage effectively. This shortage has highlighted the critical role these medications play in the daily lives of those affected by ADHD. Contact : +1 (747) 209 – 3649 E-mail : sales@trinexpharmacy.com
5-hydroxytryptamine or 5-HT or Serotonin is a neurotransmitter that serves a range of roles in the human body. It is sometimes referred to as the happy chemical since it promotes overall well-being and happiness.
It is mostly found in the brain, intestines, and blood platelets.
5-HT is utilised to transport messages between nerve cells, is known to be involved in smooth muscle contraction, and adds to overall well-being and pleasure, among other benefits. 5-HT regulates the body's sleep-wake cycles and internal clock by acting as a precursor to melatonin.
It is hypothesised to regulate hunger, emotions, motor, cognitive, and autonomic processes.
Cell Therapy Expansion and Challenges in Autoimmune DiseaseHealth Advances
There is increasing confidence that cell therapies will soon play a role in the treatment of autoimmune disorders, but the extent of this impact remains to be seen. Early readouts on autologous CAR-Ts in lupus are encouraging, but manufacturing and cost limitations are likely to restrict access to highly refractory patients. Allogeneic CAR-Ts have the potential to broaden access to earlier lines of treatment due to their inherent cost benefits, however they will need to demonstrate comparable or improved efficacy to established modalities.
In addition to infrastructure and capacity constraints, CAR-Ts face a very different risk-benefit dynamic in autoimmune compared to oncology, highlighting the need for tolerable therapies with low adverse event risk. CAR-NK and Treg-based therapies are also being developed in certain autoimmune disorders and may demonstrate favorable safety profiles. Several novel non-cell therapies such as bispecific antibodies, nanobodies, and RNAi drugs, may also offer future alternative competitive solutions with variable value propositions.
Widespread adoption of cell therapies will not only require strong efficacy and safety data, but also adapted pricing and access strategies. At oncology-based price points, CAR-Ts are unlikely to achieve broad market access in autoimmune disorders, with eligible patient populations that are potentially orders of magnitude greater than the number of currently addressable cancer patients. Developers have made strides towards reducing cell therapy COGS while improving manufacturing efficiency, but payors will inevitably restrict access until more sustainable pricing is achieved.
Despite these headwinds, industry leaders and investors remain confident that cell therapies are poised to address significant unmet need in patients suffering from autoimmune disorders. However, the extent of this impact on the treatment landscape remains to be seen, as the industry rapidly approaches an inflection point.
These lecture slides, by Dr Sidra Arshad, offer a simplified look into the mechanisms involved in the regulation of respiration:
Learning objectives:
1. Describe the organisation of respiratory center
2. Describe the nervous control of inspiration and respiratory rhythm
3. Describe the functions of the dorsal and respiratory groups of neurons
4. Describe the influences of the Pneumotaxic and Apneustic centers
5. Explain the role of Hering-Breur inflation reflex in regulation of inspiration
6. Explain the role of central chemoreceptors in regulation of respiration
7. Explain the role of peripheral chemoreceptors in regulation of respiration
8. Explain the regulation of respiration during exercise
9. Integrate the respiratory regulatory mechanisms
10. Describe the Cheyne-Stokes breathing
Study Resources:
1. Chapter 42, Guyton and Hall Textbook of Medical Physiology, 14th edition
2. Chapter 36, Ganong’s Review of Medical Physiology, 26th edition
3. Chapter 13, Human Physiology by Lauralee Sherwood, 9th edition
1. Lessons for humans from machine
learning: discovering potential drivers
of adolescent obesity
David H. Rehkopf
Associate Professor
Division of Primary Care and Population Health
Department of Medicine
Stanford University School of Medicine
University of Luxembourg
May 20, 2019
1
2. Broadest motivating question about
exposures of income, employment and
work
(Krieger et al. Am J
Public Health 2005)
Coronary heart
disease
mortality,
Massachusetts,
1989-1991, by
Census tract
poverty
2
5. Objectives of talk
Introduction: understand what is meant by “machine
learning methods”
① Control for confounding: understand how machine
learning methods may be useful in certain situations
② Subgroup effects: understand how machine learning
methods may be useful in determining what subgroups
may be most important
③ Relative importance: understand how machine learning
methods may be useful in determining the relative
importance of a large number of predictors.
Synthesis: understand basic concerns with these
applications and a few guidelines for when they may
complement or be used instead of traditional regression
approaches 5
6. the best machine learning algorithm
according to Andrew Ng…
6
[Started Machine learning MOOC at Stanford…over 8
million people have enrolled...]
8. Machine learning in general…
It generally means giving computers the ability to learn
without being explicitly programmed.
It generally means letting the computer specify the
model (or part of the model) rather than the human.
8
Machine learning for social and medical
scientists…
9. Some problems that the machine may
help to address…
① I believe the true form between my dependent and
independent variable is non-linear but I’m not sure exactly
the best function.
② I just want to explain as much variance as possible in my
dependent variable and I have a whole bunch of predictor
variables.
③ I am interested in identifying subgroups of individuals who
vary in the outcome rather than the strength of association
with a particular predictor variable.
④ I want to find the most parsimonious predictor model.
⑤ I have a whole bunch of potential control variables to use
and not sure what the best model is. 9
14. Two types of machine learning algorithms
(among very many): recursive partitioning
and random forest
Recursive Partitioning Regression Tree –
1. algorithm examines each risk factor and picks the split point within a
variable that best differentiates the outcome of interest – dividing the
sample into two nodes
2. Repeats for each of the new nodes.
3. This process continues to expand the number of nodes in the tree as long
as a new split resulted in two groups where the difference in the outcome
was significant at the p<0.05 level (or many other options for this rule).
Random forest -
4. This procedure is repeated for a large number of trees (2500), with each
one created from a subset of the overall data.
15. What makes this different?
Algorithm picks the best model from variables selected
by the investigator.
1. Can use a large number of predictor variables.
2. Best model is fit by creating models on internal subsets
of the data and testing fit on a separate part of the data.
3. Automated search for split points allows for best fit to
functional form of relationship between predictor and
outcome.
4. Tree based approach allows visualization of groups that
may differ greatly from overall population effects.
5. Random forest greatly increases robustness of process
by repeating multiple times on further subsets of the
data.
16. 16
1. Control for confounding: understand
how machine learning methods may be
useful in certain situations
+ =
Or
1. Causes exposure
2. Causes outcome
3. Not on causal pathway between exposure and outcome
4. Not caused by exposure and outcome
19. Example: Work context and
hypertension
19
POPULATION An occupational cohort obtained from 47
United States manufacturing plants (n=10,545).
Predictors. Aggregated plant characteristics: Job Satisfaction,
Feelings toward Management, Workplace Involvement,
Work Stress
Outcome. Incident hypertension
Confounders. We consider individual characteristics of
plant composition that may impact exposure and
outcome: gender, wages, race, grade, employment type.
21. Concern for confounding by county
characteristics - DAG
21
hypertension
Demographic
characteristics
of plant
Plant
characteristics
County characteristics
22. Problem of area level confounders – good
news/bad news.
Bad news there are likely to be confounding variables that are not at
the individual level.
Bad news because exposures may be ecological or area level,
individual level covariates may not be useful in blocking all the
effects of more macro-social variables.
The good news is that there are free and comprehensive data sources of
potential area level confounders that are available.
The bad news is that there is not strong evidence for the choice of
which of these measures is most appropriate for a particular
outcome.
Bad news is that functional form of the dependence of each with an
outcome of interest is primarily unknown.
Good news is that most of this bad news can be addressed by machine
learning methods.
23. County characteristics (68)
23
'Census 2000 total resident population, 4/1/00', 'Percent population change, 4/1/00 to 7/1/05', 'Births 4/1/00 to 7/1/00', 'Deaths
4/1/00 to 7/1/00', Net international migration 4/1/00 to 7/1/00', 'Census 2000 housing units, 4/1/00', 'Percent housing unit
growth, base 4/1/00 to 7/1/05', 'Percent of resident population aged 0 to 14 years, 7/1/05', 'Percent of resident population
aged 15 to 64 years, 7/1/05', 'Percent of resident population aged 65 years and over, 7/1/05', 'Percent of resident population
aged 85 years and over, 7/1/05', 'Sex Ratio, 7/1/05', 'Median age of total resident population, 7/1/05', 'Median age of male
resident population, 7/1/05', 'Median age of female resident population, 7/1/05', 'Percent of resident population white alone,
7/1/05', 'Percent of resident population black alone, 7/1/05', 'Percent of resident population American Indian and Alaska
native alone, 7/1/05', 'Percent of resident population Asian alone, 7/1/05', 'Percent of resident population Native Hawaiian
and other Pacific islander alone, 7/1/05', 'Percent of resident population of two or more races, 7/1/05', 'Percent of resident
population non-Hispanic, 7/1/05', 'Labor force, annual average estimate, 2005’, 'Unemployment rate, annual average
estimate, 2005', '2004 ERS Economic Type', '2004 ERS Policy Type: Housing stress', '2004 ERS Policy Type: Low-
education', '2004 ERS Policy Type: Low-employment', '2004 ERS Policy Type: Persistent poverty', '2004 ERS Policy Type:
Population loss', '2004 ERS Policy Type: Nonmetropolitan recreation', '2004 ERS Policy Type: Retirement destination', '2003
ERS Urban Influence Code', '2003 ERS Rural-Urban Continuum Code', 'Population (persons), 2005', 'Per capita personal
income (dollars), 2005', 'Contributions for government social insurance ($1,000s), 2005', 'Contributions for government social
insurance: Employee and self-employed contributions for government social insurance ($1,000s), 2005', 'Contributions for
government social insurance: Employer contributions for government social insurance ($1,000s), 2005', 'Adjustment for
residence ($1,000s), 2005', 'Net earnings by place of residence ($1,000s), 2005', 'Dividends, interest, and rent ($1,000s),
2005', 'Personal current transfer receipts ($1,000s), 2005', 'Per capita net earnings by place of residence, 2005', 'Per capita
personal current transfer receipts, 2005', 'Per capita income maintenance, 2005', 'Per capita unemployment insurance
benefits, 2005', 'Per capita retirement and other benefits, 2005', 'Per capita dividends, interest, and rent, 2005', 'Average
earnings per job, 2005', 'Average wage and salary disbursements per job, 2005', 'Average nonfarm proprietors'' income,
2005', 'Standardized score for mean temperature for January, 1941-1970', 'Standardized score for mean hours of sunlight for
January, 1941-1970', 'Standardized score for mean temperature for July, 1941-1970', 'Standardized score for mean relative
humidity for July, 1941-1970', 'Standardized score for land surface form typography code', 'Standardized score for natural log
of percent water area', 'ERS Natural Amenity Scale', 'ERS Natural Amenity Rank', '2004 IECC (supplement to 2003 IECC)
Climate Zone', '2004 IECC (supplement to 2003 IECC) warm-humid counties', 'Koppen classification corresponding to 2004
IECC Climate Zone', 'America Climate Region', 'Index crime rate (per 100,000 persons), 2004', '2004 presidential election:
Percent of votes for Bush', '2004 presidential election: Percent of votes for Kerry', '2004 presidential election: Percent of
votes for other candidates'
24. Data adaptive search for County Characteristics Predicting
Exposures and Outcome
(variable importance measures from Random Forest
Algorithm)
24
Relative
importance
Job Environment
Exposure
Hypertension
1.
Social insurance
expenditures
Income
maintenance
2. Earnings
Unemployment
benefits
3. Population size
Retirement
benefits
4.
Income
maintenance
Earnings
5. % white Population size
6.
Retirement
benefits
Urbanicity
7. % pop age 0-14 Urban influence
25. Estimates of Association between Workplace Social
Characteristics (baseline and change 2006-2008) and
Incident Hypertension (2006-2008)
25
Baseline Change
Model 1 –
individual
level
covariates
Model 2 – also
including 10
ecological
confounders
Model 1 –
individual
level
covariates
Model 2 – also
including 10
ecological
confounders
Estimate (SE) Estimate (SE) Estimate (SE) Estimate (SE)
Job
Satisfaction
-0.013 (0.0067) -0.060 (0.032) 0.002 (0.00093) 0.037 (0.041)
Perception of
management
0.0016 (0.011) -0.051 (0.042) -0.054** (0.012) -0.0019 (0.068)
Workplace
involvement
-0.0261* (0.0077) -0.040 (0.024) -0.0086 (0.0088) 0.0030 (0.0040)
Work stress 0.0047 (0.010) 0.036 (0.034) 0.027* (0.014) 0.032 (0.054)
26. 2. Subgroup effects: understand how
machine learning methods may be
useful in determining what subgroups
may be most important
!
27. Limitations to traditional approach to
subgroup analysis and interaction in a
regression framework
Traditional regression approach: A priori decide on
subgroups to examine or look for subgroup
effects ad hoc in the data.
Limitations: Literature may not be rich enough to
know what is important, multiple comparison
issues, can only examine a limited set of
covariates.
28. 28
Regression trees
Recursive partitioning is an automated method for creating
a regression tree.
1. Splitting (partitioning)
2. When to stop (terminal nodes)
3. Pruning (optimized by 10 fold cross-validation)
Implement using rpart package in R, by Terry Therneau
and Beth Atkinson, based on the work of Leo Breiman
29. NHLBI Growth and Health Study, Research
question.
Question: What subgroups best predict change in BMI,
allowing for interactions and non-linear prediction?
NHLBI Growth and Health Study (1987-1997), age 9-10 at
baseline.
A total of 2379 girls (1213 black and 1166 white)
Detailed social, environmental, economic, psychological,
behavioral and dietary data
30. Social and individual factors predicting
change in BMI, girls, age 9-19
Model rpart() and ctree() packages in R
Outcome BMI change from 9-19
Predictors
Dietary intake and eating behaviors: total kcal, % kcal from fat, % kcal from protein, eats
breakfast, eats snack food, eats fast food, eats while watching television, eats with soda
on the table*, family eats dinner together*, eats dinner alone*, time to eat dinner*
Behavioral: physical activity
Psychological: body dissatisfaction (EDI)**, bulimia (EDI)**, distrust (EDI)**, drive for
thinness (EDI)**, ineffective (EDI)**, interoceptive awareness (EDI)**, maturity fears
(EDI)**, perfection (EDI)**, self-worth (Harter), physical appearance (Harter), social
acceptance (Harter), athletic competence (Harter), behavioral conduct (Harter),
cognitive restructuring (Tobin), express emotions (Tobin), self criticism (Tobin), anxiety
(Reynolds)**, perceived stress (Cohen)**, emotional eating index
Social: number of siblings, race (black vs. white), male currently in household, income,
education
Parent Health: BMI*, self-reported health*, physical activity (self-reported)*, importance of
exercise*, depression*
32. Second application: For whom is BMI
most effected by the EITC policy?
Research question: What is the impact of a large
poverty reduction policy (the Earned Income
Tax Credit) on child BMI?
1. We may care about subgroups of the
population who do not benefit.
2. It may give us insights into why or why not the
policy is effective.
3. We may want to change or supplement the
policy to reach all population groups. 32
33. Model based recursive partitioning
“Party” package in R, using the “mob” function.
Data: NLSY79 Children, ages 2-18, years 1984-2008
Outcome: BMI percentile
Fit a structural part of the model with known confounders
and exposure of interest, and then scan over remaining
covariates to examine if effects differ by subgroups.
bmimob1<- mob(bmipctdif ~ adjeitc + sex | agemos + year + mar + div
+ hrswrk + dep + region + flchild + southbirth + southchild +
rosen + rotter + pearlin + cesd + black + other + region1 +
region2 + region3 + urbanR + urbanchildR + eduR + afqtR + momeduR
+ dadeduR + fbR + hisp + adjinc + healthySum + fastfoodSum +
unhealthySum, control = mob_control(minsplit = 26), data =
eitcNLSY, model = linearModel)
33
35. 3. Relative importance: understand
how machine learning methods may be
useful in determining the relative
importance of a large number of
predictors.
35
36. 36
Random forest approach
Creates multiple decision trees based on random selection
of observations
Evaluates how good decision trees are in predicting
outcomes among those individuals not used to create the
decision tree
Variable importance measure is the average change in node
impurity comparing final model with model with single
randomized variable of interest.
Implementation using randomForest package in R, R port
by Andy Liaw and Matthew Weiner based on original
Fortran code by Leo Breiman and Adele Cutler
38. Social and individual predictors of
adolescent obesity
Model rforest() package in R
Outcome BMI age-sex specific percentile change from 9-19
Predictors
Dietary intake and eating behaviors: total kcal, % kcal from fat, % kcal from protein, eats
breakfast, eats snack food, eats fast food, eats while watching television, eats with soda
on the table*, family eats dinner together*, eats dinner alone*, time to eat dinner*
Behavioral: physical activity
Psychological: body dissatisfaction (EDI)**, bulimia (EDI)**, distrust (EDI)**, drive for
thinness (EDI)**, ineffective (EDI)**, interoceptive awareness (EDI)**, maturity fears
(EDI)**, perfection (EDI)**, self-worth (Harter), physical appearance (Harter), social
acceptance (Harter), athletic competence (Harter), behavioral conduct (Harter),
cognitive restructuring (Tobin), express emotions (Tobin), self criticism (Tobin), anxiety
(Reynolds)**, perceived stress (Cohen)**, emotional eating index
Social: number of siblings, race (black vs. white), male currently in household, income,
education
Parent Health: BMI*, self-reported health*, physical activity (self-reported)*, importance of
exercise*, depression*
43. Synthesis 1: Control for confounding: understand
how machine learning methods may be useful
in certain situations
Limitations:
1. Must be careful not to include colliders
2. Could potentially reduce power
Strengths:
1. Decreasing bias
2. Efficient
3. Reproducible
When:
1. Causal model not well understood
2. Clear priors about causal ordering
3. Large number of possible potential confounders 43
44. Synthesis 2: Subgroup effects: understand how
machine learning methods may be useful in
determining what subgroups may be most
important
Limitations of Recursive partitioning based approaches:
1. Not completely hypothesis driven (but inputs are)
2. Limited in cross-validation
Strengths:
1. Hypothesis generating
2. Checking robustness of results
3. Explicit approach for examining heterogeneity
4. Considering subgroup and interactions as fundamental
When:
1. A prior concerns or interest in heterogeneity of effects
2. Unclear priors in literature about where subgroup effects may be
most important.
3. Results can be replicated in another dataset 44
year
p < 0.001
1
≤ 1998 > 1998
agemos
p < 0.001
2
≤ 84 > 84
agemos
p < 0.001
3
≤ 31 > 31
Node 4 (n = 427)
-393 4323.5
-119
119
black
p = 0.028
5
≤ 0 > 0
Node 6 (n = 1434)
-393 4323.5
-119
119
Node 7 (n = 633)
-393 4323.5
-119
119
agemos
p < 0.001
8
≤ 149 > 149
afqtR
p = 0.002
9
≤ 7282 > 7282
year
p = 0.004
10
≤ 1994 > 1994
Node 11 (n = 169)
-393 4323.5
-119
119
Node 12 (n = 35)
-393 4323.5
-119
119
black
p < 0.001
13
≤ 0 > 0
dadeduR
p = 0.012
14
≤ 4 > 4
Node 15 (n = 58)
-393 4323.5
-119
119
Node 16 (n = 760)
-393 4323.5
-119
119
Node 17 (n = 428)
-393 4323.5
-119
119
Node 18 (n = 453)
-393 4323.5
-119
119
Node 19 (n = 909)
-393 4323.5
-119
119
45. Synthesis 3: Relative importance: understand how
machine learning methods may be useful in
determining the relative importance of a large
number of predictors.
Limitations:
1. impacted by differential measurement error
2. direction of cause ambiguous
Strengths:
1. Broader view of potential causes
2. Considering subgroup and interactions as fundamental
3. Multiple regression trees result in results that are typically more
stable than traditional regression results
When:
1. Prior knowledge of large number of potential risk factors
2. large number of well measured covariates
3. Combine with matching approaches for causal inference 45