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Solved: For the unity feedback system shown in Figure

Control Systems Engineering | 7th Edition | ISBN: 9781118170519 | Authors: Norman J. Nise ISBN: 9781118170519 162

Solution for problem 3 Chapter 7

Control Systems Engineering | 7th Edition

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Control Systems Engineering | 7th Edition | ISBN: 9781118170519 | Authors: Norman J. Nise

Control Systems Engineering | 7th Edition

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Problem 3

For the unity feedback system shown in Figure P7.1, where G s 60 s 3 s 4 s 8 s2 s 6 s 17 find the steady-state error if the input is 80t 2u t . [Section: 7.2]

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Prelim Study Questions PAM 3280: Fundamentals of Population Health Midterm: Oct 1 (in class) *****REMEMBER TO BRING A CALCULATOR AND PEN/PENCIL!!! *****YOU WILL HAVE 1 HOUR and 15 MINUTES TO COMPLETE THE EXAM!!! Formulas YOU need to know: Incidence Rate = (new cases in specified time period) / (total population at risk (does not include people who already have/had condition before time period or year being considered)) * (1000 or whatever number you want) Period Prevalence = (count of existing cases at any time during period of observation­ people who got the condition) / (mid­period population that is alive) * 100 Point Prevalence = (count of existing cases at point of observation­ people who got the condition and have the condition) / (count of population at point of observation) *100 Infant mortality rate = (number of deaths under 1 year of age, in time period)/ (total number of live births in time period) Age­specific rates = (number of deaths in specific age group, in time period) / (total population in specific age group, in time period) Crude death rate = (number of deaths) / (total mid­year population) Dependency ratios = Sex ratio = (# of males) / (# of females) * 100 Formulas provided during exam (see posted formula sheet): E=event D=deaths B=births P=population Neonatal mortality rate: D B x 1,000 <28 days/ Post neonatal mortality rate: D B x 1,000 28 days­1 year/ Cause­specific mortality rate: D /P x 1c0,000 (where c = specific cause) Maternal mortality ratio: D /B x p00,000 (where p = puerperium) Proportionate mortality ratio: D /D x c00 (where c = specific cause) Case fatality ratio: D /Pc x c00 (where c = specific cause or P diagnosed with the disease) If you were provided a table of population and health data, you should be able to calculate the percents, rates and ratios listed above. ***ALWAYS INCLUDE THE APPROPRIATE METRIC OR YOU WILL NOT GET FULL POINTS!*** If you were provided data in life table, you should be able to report/calculate the following statistics if requested or if some of the data were crossed out in the table: Life expectancy at birth (e ) 0 Life expectancy at age x (e ) = Txx/ Ix = total number of person years lived about age x / number surviving to age X Probability of dying between ages x and x+n ( q ) = the pron xtion dying = nDx / Ix = Number dying between age interval / number surviving to age x Number of persons from the original synthetic cohort of 100,000 live births, who survive to the beginning of each age interval (l ) = nDx/xnQx = number dying between age interval / proportion dying or probability of dying Number dying in a given age interval ( d n x (nQx) (Ix) = probability of dying in age interval * number surviving Determine median age of death­ locate in nQx, where proportion is near/btwn .5, also look at Ix, when number surviving is around half of original population, note that age interval as where median probably is Calculations: When calculating a health statistic, show your calculations (e.g., write out the numerator and the denominator)! That way, if you make a basic math error when making your calculation, you can still receive partial credit for using the correct formula and/or for using the correct values in your formula. Be sure to define your rates/metric (e.g., deaths due to injury per 1,000 women age 15­17) or indicate when you’re reporting a percent (e.g., 15%). Study Questions: 1. What is “population health” What are some key differences between the “medical model” of health versus the “population health” model ● Population health is an approach that sets to improve the health of the entire population (a group of individuals) and the distribution of those outcomes (reducing health inequities); it is much more than risk and clinical factors; recognizes that health is shaped by life experiences, social, cultural, environmental and political factors ● Pop Health is the study of why some people are healthy, while others are not. It focuses on the mean/central health outcomes/determinants of a population AND the distribution of health outcomes/determinants of a population ○ Goes beyond risk and clinical factors, approach recognizes that health status is shaped by life experiences, social, cultural, economic, environmental, and political factors It is the health outcomes of a group of individuals, including the distribution of these outcomes and determinants, within the group ● A conceptual framework for thinking about why some populations are healthier than others as well as the policy development, research agenda, and resource allotting ● Chronic disease management Medical Model Population Health Model health as absence of disease health as state of physical, mental, social well­being focus on individual risk focus on social structures and process affects ill or high­risk individuals affects entire population clinical/reactive – drugs, comprehensive/proactive – reducing equipment contribute to health social disparities that contribute to health health care central to producing address health determinants and health health disparities is central to producing health focus on disease focus on health treatment prevention and health promotion 2. 2) If you approach health from a population health perspective, you are never satisfied with just the overall mean value (of a population, on some indicator). What does this mean static displays of data may mask important variation ● within group differences (disparities) ● between group diff (benchmarking) ● trends over time never be satisfied with a single statistic (mean / %) The mean isn’t representative of an entire population and it ignores the entire group of people above and below the central measure. 3. What three epidemiological or health eras did Susser and Susser (1996) describe and what are their basic features (Era, paradigm, analytical approach, preventive approach) Can we say with certainty we currently live in one specific era Era Paradigm Analytic Approach Preventive Approach sanitary statistics (first Miasma­ poisoning clustering of morbidity sanitation, drainage, half of 19 ) from bad soil, air, and and mortality sewage water (foul smell) Infectious disease epi germ theory­ lab isolation & culture interrupt transmission (late 19 century­first organisms cause from disease sites, (vaccine, quarantine, half of 20 century) disease (single agents experimental antibiotics) relate to specific transmission, disease) reproduction of lesions Chronic disease epi black box­ exposure analytic strategy­ risk control risk factors by th (later half of 20 related to outcome ratio of exposure to modifying lifestyle, century) without considering outcome at individual environment, agent intervening factors level in populations (“risk factor epi) (statistical association known regardless of mechanism) ○ no because they are constantly evolving ○ we are changing from the era of chronic disease epidemiology to the era of eco­epidemiology ■ we need to start looking more upstream 4. Who is the “Father of Modern Epidemiology” and why What was the significance of removing the handle off of the Broad Street pump ● John Snow. He conducted first natural experiment that confirmed that cholera was water­borne; used geographic statistics to see that cholera deaths were clustering around one pump (increased death incidence) – broke water pump, and death rate decreased; disproved miasma theory 5. What is the demographic transition ● observable pattern of changes in fertility and mortality. Mortality rate falls first, then fertility rates decline ­> Decreasing fertility + reductions in age­specific mortality = pop growth ● Stage 1: Pre­industrial society; death and birth rates high and in balance = very slow pop growth ● Stage 2: Developing countries­improvements in sanitation, food supply, personal hygiene = reduced mortality = population growth ● Stage 3: Developing countries­access to contraception and increased urbanization, increased education of women, increased wages = declines in fertility = death and birth rates low and in balance = population growth levels off ● Stage 4: Populations may begin to decline @ fourth stage if death rates decline as low as possible and fertility declines below replacement 6. What is the epidemiological transition ● fewer people dying of infectious diseases = survive to older ages in 1900, infectious diseases leading cause of death → 2000: chronic diseases associated w/ individual lifestyle 7. What are the 5 leading causes of death in the US in 2011 Heart Disease, Cancer, Stroke, Chronic lower respiratory diseases, accidents 8. What are McKeown’s and LaLonde’s theses and why were they important to the evolution of population health ● McKeown thesis­ economic and social conditions (not medical therapies) reduced mortality of infectious diseases in 1700s. Important because it raised the question of whether public health issues should have narrow interventions focused at individual/community level or broad measures that address the social, political, and economical factors that play a large role on health status on a population level. ● LaLonde thesis – focusing on changing lifestyles or social/physical environments would result in more improvments in health than spending more money on existing health care systems. ● Important because it lead to many proactive health promotion programs that increased awareness of health risks associated with certain personal behaviors and lifestyles (smoking, drinking, nutrition, fitness) ● proposed concept of “health field” ­ health field should be broken down into: human biology, environment, lifestyle, and health care organization 9. What is meant by “proximal” versus “distal” causes of death/illness ● Proximal causes are more behavioral risk factors, health outcomes – more downstream ● distal causes of death – are upstream; broader structural causes (social determinants, policies, programs, political climate) 10. Why are counts of health events (e.g., deaths, births, hospital visits) useful but not the ideal metric to use to describe the health of a population Why might (or when might) rates a better measure What is the difference between a crude and an age­standardized rate ● Counts: Useful for describing actual impact of a disease in a specific pop. Helpful for services planning (knowing the actual number of persons with a disease or condition gives information about how it will impact utilization). Con: Can’t compare across groups. ● Rates are a better measure because they permit comparisons across groups (need to know group’s population size per time period) ● crude rate­ total number of cases in given time period / total number of persons in a population; is an overall average rate of disease; confounding factors are not considered ● age­standardized rate­ accounts for age, which is a confounding factor; if it is unequally distributed among populations compared, cold skew results; unadjusted rates may make it appear that older individuals. “This is important when looking at cancer rates because cancer is a disease that predominantly affects the elderly. So if cancer rates are not age­standardised, a higher rate in one country is likely to reflect the fact that it has a greater proportion of older people.” 11. If shown different population pyramids, could you identify which population is younger Older ○ young population pyramid: ■ ○ ○ ○ ○ ○ ○ old population pyramid: ■ ○ stationary pyramid: ■ 12. What is the “feminization of aging” ○ male mortality is consistently higher than female mortality ■ implication: the world sex ratio is constantly decreasing with age 13. What are the biosocial determinants of health The biosocial determinants of health are: age, sex, race­ethnicity (“ascribed) 14. What is the difference between a risk factor and a risk marker or achieved versus ascribed factors) ○ risk marker: ■ something that cannot be changed ■ instead helps provide an idea of risk within a population ■ examples: age, gender, race/ethniticty ○ risk factor: ■ some that can be changed to descrease your risk of death ■ examples: smoking, drinking, risky behaviors, etc 15. What is a dependency ratio Sex ratio If given age or sex data, could you calculate these ratios ○ Dependency Ratio: number of dependents (young or old enough to not work so like less than 15 or older than 65) per 100 working age resident ■ indirect measure of challenges or opportunities faced by a population in terms of resource allocation to their young and old members ● the higher the ratio, the higher the economic burden ■ limitation: doesn’t take into account who is actually working or their economic well­being ■ Total dependency ● ■ Youth dependency ● ■ Aged Dependency ● ○ Sex ratio: Number of males per 100 females→ describes the gender composition of a population ■ formula: (#males/#females)*100 ■ sex ratio>100: male excess ■ sex ratio<100: female excess 16. What is morbidity Comorbidity ● morbidity is a diseased state, disability or poor health ● comorbidity is a disease that is a result of, or has strong relation to, a primary disease – ex: diabetes as comorbidity of obesity 17. What is the difference between incidence and prevalence ● incidence­ rate at which new cases of a disease in a given population in a specific time period ● # of new cases in time period/ # at risk of case event in time period ● key: change in health status marks event ● prevalence­ proportion of currently existing cases in specific population in a specific time period ● # of existing cases in pop in specified time / # of individuals in pop x 100 (is a %)) ● key: number of existing cases ( both new and pre­existing) observed in time period 18. If given population and health data, can you calculate incidence, period prevalence, and point prevalence ● Point prevalence: prevalence of disease at a single point in time ● numerator: count of cases at point of observation ● denominator: count of population at point of observation ● Period prevalence: prevalence of a disease at any time during a specified period ● numerator­ count of cases at any time during period of observation ● denominator­ mid­period population 19. If given examples of health statistics or survey questions, could you determine whether the statistic/question was asking about incidence, point prevalence, or period prevalence ● Do you currently smoke – point prevalence ● In the past year, were you diagnosed with arthritis incidence ● Have you ever been diagnosed with hypertension period prevalence 20. What is the effect on your incidence rate if your population denominator was not specific to those people at risk of the event in the numerator or if your numerator included repeated (or duplicated) cases ● If the denominator is not specific to the people at risk of the event in numerator, then individuals who already have the disease at the beginning of the study period are not actually new cases. Repeated cases would not be considered new cases (unless they are of the recurrent type like gonorrhea which have a long enough period between disease episodes) because they are considered “relapses” 21. Which is a measure of risk: incidence or prevalence Why Incidence is a measure of risk because it is a measure of the frequency of new disease events occurring in a population. Prevalence is not a measure of risk because it ignores duration of disease and/or death 22. What is the mathematical association between incidence, prevalence, and duration If incidence increases, must prevalence necessarily increase ● Prevalence = incidence x duration ● if incidence and duration increases, prevalence increases; if incidence and duration decrease, prevalence decreases ● if just incidence increases, it doesn’t mean that prevalence will increase → disease could have rapid recovery or rapid fatality rate (approx. low prev) ● high prevalence­ high incidence or prolonged survival w/o cure, or both 23. What are common measures of frequency and intensity and when would you mostly likely measure these ● binge drinking study: ○ excessive alcohol use accounted for an estimated avg of 23,000 deaths and 633,000 years of potential life lost (PLL) among women and girls in the United States each year during 2001­2005 ○ binge drinking accounted for more than half of those deaths and YPLL ○ prevalence: number of women who responded yes to having consumed 4 or more alcoholic beverages on one occasion in past 30 days divided by n ○ frequency: among women who said yes to binge drinking, what was the total number of episodes of binge drinking in past 30 days ● intensity: among those reporting one or more episodes of binge drinking, average largest number of drinks consumed during past 30 days 24. Why is it important to have an international classification of disease (ICD) system ● The ICD system is important because it provides standardization in reporting and monitoring diseases. This makes it easier for universal comparison and sharing of data in a consistent manner between hospitals, regions and countries over periods of time. 25. Can you identify the difference between primary and secondary data ● Primary: Data collected specifically for the purpose of a study – expensive to collect Secondary: Data collected for other purposes but made available for use by others (e.g., large, national health surveys; Census; health insurance claims data vital statistics; etc.) 26. Why are crude rates crude How do you calculate crude death rates ● Crude rates are crude because in the denominator, the entire population is included because everyone is at risk of dying. however the denominator is not adjusted for changes in population age structure over time → can’t compare across populations ● crude death rate= # death / # of people in population x 100000 27. What is the general shape of a mortality curve in a developed (developing) country (i.e., J shaped versus U shaped) What does this shape reflect In developing countries, the mortality curve is more U shaped due to high infant mortality, which then declines once the 5 year threshold passes. Then mortality increases with age. Developed countries have J­shaped mortality curve. Be able to, given data, calculate the mortality indicators discussed in class and identify pros/cons of each. ● Age specific mortality rate: # of deaths in given yr for age x / population in age x * 100000 ● pro: reflects risk of death by age group; con: need one rate per each age group to assess population’s mortality ● Race Specific Mortality Rate: # of deaths in given yr for race x/pop in race x * 100000 ● still crude since not age­adjusted ● Cause specific mortality: # of deaths from given cause c /population x 100000 ● not ideal bc denominator isn’t specific to cause ● Maternal Mortality Ratio (type of cause specific mortality risk) ­ # of maternal (puerperal) deaths per 100000 births (pueurperal → after delivery of baby up to 42 days) ● main indicator of safety of pregnancy and childbirth ● MMR= D /live Pirths x 100000 ● Case Fatality Ratio­ proportion of people with specific disease c in given time period who die w/in that time period ● CFR= D /P xc10c ● measure of severity of disease and measure of quality of care ● denominator limited to those only who have disease (unlike mortality which includes entire population – w/ disease or w/o but at risk) mid population in that year 28. Why is proportionate mortality not a measure of risk ● it is the proportion of all deaths attributed to a particular cause in given time period ● PMR= D /D x c00­­ number of deaths by cause/number of deaths (100) ● not a measure of risk – increase in PMR for particular cause doesn’t mean incidence of death increased; just that proportion of all deaths due to it increased relative to other causes 29. What is the general trend of infant mortality in the US and how does the US compare to other developed countries in terms of infant mortality Which group of mothers (race) has the highest infant mortality th ● In the 20 century, there was a dramatic decline in infant mortality (from 1940 – 2010 has decreased by 86%). The US infant mortality rate has plateaued in the first few st years of the 21 century. th ● Compared to other developed countries, the US ranks 30 in infant mortality ● Black mothers have the highest infant mortality – factors → low birth weight infants, SES, access to medical care 30. What is the difference in cause of death contributing to neonatal mortality versus postneonatal How might this impact health services What are the two main causes of infant mortality in the US (can you define them) ● Infant mortality= # of infant deaths (< 1 yr) / live births * 1000 ● Neonatal mortality rate= D <28 ays /live births * 1000 ● medical care­ trauma at birth, genetic abnormalities ● Post­neonatal mortality rate= D 28days to <1yr / live births * 1000 ● environmental cause: SIDS, accidents, SES ● Two main causes of infant mortality in the US: ● low birth weight – birth weight less than 2500 grams (US has greater % of LBW babies than other developed countries) ● pre­term births: less than 37 wks of gestation (US has greater % of pre­term births than other developed countries) 31. How is cause of death determined ○ from the death certificate ○ the underlying cause is defined by WHO as “the disease or injury which initiated the train of events leading directly to death, or the circumstances of the accident or violence which produced the fatal injury” 32. When examining mortality rates over time (or any other health trend), why is it important to be able to distinguish between real versus artifactual difference (examples of each) ○ real: changes in age structure of population, changes in survival (technological or clinical innovation or tx) changes in incidence ○ artifactual: changes in disease recognition, definition, classification; changes in reporting accuracy or population identification 33. What is the difference between life expectancy and life span ● Life expectancy is THE MEAN. It’s a basic indicator of population health and is average number of yrs of life remaining for persons who have attained a given age (ie: life expectancy @ age 65­ if someone reached the age of 65, how much longer are they expected to live) Life span­ maximum amount of years possibly at ideal health 34. What are the main components of a life table What is the key output statistic of a life table Why is life expectancy considered a “hypothetical” measure Why can life expectancy be compared across populations and time (if the assumptions of when death occurs in an age interval are consistent between years or pops) ● life table­ summarizes how mortality varies with age ● Components: ● Column 1: Age interval between 2 exact ages (usually 5 years except first 2 rows) ● Column 3: Probability (proportion) of dying between ages x to x+n ● Column 4: # of persons who survive to beginning of each age interval ● Column 5: # dying within age interval out of 100000 live births ● Column 6 : Person­years lived (nLx) – number of person­years lived in age interval ● Column 7: Total number person years lived (T ) x ● Column 8: Life expectancy (e ) – average umber of years of life x remaining to be lived for persons who have achieved a given age x ● Formula: T /l x x ● Key Statistic­ life expectancy at birth (e ) o ● Radix is I ,0the starting number of newborns in life table (often set at 100,000) ● Life expectancy is “hypothetical” measure because person experiences specified age­specific mortality rates as they live out their lives ● Life expectancy can be compared across populations and time because they are standardized/produce age­adjusted metrics 35. Which column of a life table is used to generate a ‘survival curve’ What can graphing a survival curve tell us about the health of a population What is meant by the “rectangularlization of mortality” or “compression of mortality” If given two survival curves, could you identify which represents a healthier or older population ● I –x umber of persons surviving at the beginning of each given age interval ● graphic survival curve tells a lot about health of population b/c flatter and more horizontal curve at lower age= lower mortality and more vertical at older ages ● rectangularization of mortality­ large amount of population at younger ages are surviving and then in old age, start dying more → people living into old age and then dying; if curve is more “triangle” it means babies are dying earlier and continue to do so into older ages (high mortality rate) ● compression of mortality requires policy implementation that would reduce child mortality and shift mortality curve of individuals to the right, meaning that individuals are living to older ages 36. How are education and gender associated with life expectancy and the shape of mortality (Brown et al., 2012 slides included in slide deck) education matters for a group’s mortality; women with 13+ yrs of education have greatest nd rectangularized mortality (highest compression of mortality); women w/ 12 yrs came 2 , men w/ 13+ yrs came third; men with no college had lowest 37. What are the general race and gender trends in life expectancy at birth ● Hispanic females had highest “rectangularized” mortality ● everyone is comparable at lower ages ● non­hispanic blacks have highest mortality (lowest survival) at younger ages 38. What is the difference between life expectancy and healthy life expectancy Why is measuring healthy life expectancy important What may be wrong with focusing solely on increasing life expectancy as a public or population health goal ● Life expectancy is just the average number of years a person might be expected to life. Healthy life expectancy is avg number of remaining years a person is expected to live in “full health” ● It is important to measure health life expectancy because just extending life expectancy is useless if the extra years are spent in disability or disease, thus lowering quality of life. ● It is wrong to just focus on increasing life expectancy because then there will be lower quality of life due to increased age and increased prevalence of chronic disease, meaning more money spent on healthcare. 39. Can you identify health expectancies and gaps in a graph – i.e., can you identify the different survival curves in a graph that depict proportion surviving versus proportion surviving in good health If you had both a survival and morbidity curve graphed, for any given age could you calculate the proportion of time spent in good health nLx= total number of person years lived for population in age interval x to x+n Proportion of years lived in health state: nL’x= (n[pi]x) L n x Healthy life expectancy: e’x= T’ /I x x nLx= total number of person years lived for population in age interval x to x+n Proportion of years lived in health state: nL’x= (n[pi]x)nLx Healthy life expectancy: e’x= T’x/Ix 40. What is meant by the term “compression of morbidity” compression of morbidity is important , want policy implementation that will compress morbidity and shift it to the right ­­ ideal: MORBIDTY CURVE perfectly overlays Mortality – meaning that you are dying health 41. If given the number of life years lived in a given life table age interval, and the percent of the population healthy (or unhealthy) in that age group, could you calculate healthy life years in that age interval See list of lifetable stats on page one of this guide to see what else you may need to be sure to know. ○ L = total number of person­years lived for the population in age n x interval x to x+n ○ Assume π = p npx tion of each age group healthy (from health surveys) ○ Proportion of person­years lived in a healthy (‘) state: L’ = n x (nπx)*n x 42. Why is it important to measure quality of life and well­being (and not just morbidity/mortality) ● non fatal health outcomes from diseases/injuries are important in promotion and monitoring of individual and population health ● life expectancy has improved worldwide but disability is more prevalent reflects appreciation for not only how long one lives, but how well one lives 43. According to the WHO, what is the difference between impairment and disability What is the association between age and prevalence of disability What is the leading cause of disability in the US On average, who reports greater morbidity and lower HRQOL, men or women ● impairment­ loss or abnormality of function or structure at organ level (loss of eyesight, amputation) ● disability­ restriction in participation that results from lack of fit between individual’s personal limitations and characteristics of social and physical environment ● as age increases, prevalence of disability increases ● leading cause of disability in US: arthiritis (and musculoskeletal problems) ● 2 : heart disease, 3 : stroke, 4 : cancer, 5 : mental health problems ● women, older persons (physical), younger persons (mental), non­white race/ethnic groups (except A/PI), those with less education, speak other lang than English, and w/ disability – report higher morbidity and lower HRQOL 44. How is disability measured in the US What are ADLs and IADLs and why are they important ● disability measured on ability to perform activities of daily living ● ADL – used to asses basic self­care tasks of every day life – walking, eating, bathing, dressing, toileting, moving from one place to another ● IADL­ instrumental activities of daily living – used to access independent living ability ● cooking, driving, shopping, keeping track of finances, using phone/computer, taking meds 45. What is the association between disability and healthcare expenditures among the Medicare population (Chan et al 2002) increased disability associate with higher mean health care costs across multiple cost categories (except for services like cost for admission/visit/prescription) 46. How is subjective health measured How is health­related quality of life (HRQoL) typically measured (e.g., what are the different components) ● subjective health is measured as “self­rated health” → Would you say in general your health is: excellent, very good, good, fair, or poor → strong predictor of mortality, morbidity and biological markers ● HRQOL­ type of subjective health (involves physical, mental, social health + role functioning); ● associated w/ self­reported chronic diseases (diabetes, arthritis, hypertension) and risk factors (BMI, physical inactivity, smoking status) ● determines burden of preventable disease, injuries, disabilities ● HRQOL­14: 14 questions to measure physical and psychosocial well­being 47. According to the video, In Sickness and in Wealth, how does social class “get under your skin” (biological mechanism) What key hormone is involved What would happen to the prevalence and/or extent of health disparities if we eliminated poverty Would disparities be completely eliminated ● CORTISOL – too much release of cortisol raise blood glucose levels which increase blood pressure and heart rate ● Stress in short­term is good but when stress is long­term, cortisol that is pumped in bloodstream pushes glucose which increases blood pressure and builds atherosclerosis which leads to heart disease and impairs immune system, thus weakening healthy status. ● lack of control over life and situation also factor ● If poverty was eliminated, disparities would not be eliminated. It would just shift the reference frame by compressing it because SES positioning gradation (comparing self to others), and other factors like race/ethnicity, education, etc play a role in health status. ● the prevalence of health disparities would still exist, it would just shrink the gap between the lowest income people and the highest income 48. True or False: Education explains most of the difference in black­white infant mortality rates. FALSE! Even a white woman with high school education has better infant mortality outcomes than black woman with college education Even with controlling SES, huge disparity still exists – power, stress, discrimination which contributes to stress­­ power, stress, discrimination!!!!!!!!!!!!!!!!! Black women have highest rates of low­birth weights. 49. True or False: According to Sir Michael Marmot (video), behaviors explain about half of social gradient in mortality. What are some examples of how social policies drive health (both good and bad) FALSE! Behaviors explain 25% of social gradient in mortality. Health is largely determined by social class. Review video discussion questions covered in class. 50. What are “social determinants of health” (SDH), what are some specific examples, and how might they directly or indirectly impact health (mechanisms) (How do both employment and education directly and indirectly impact health). What are distal (upstream) versus proximal (downstream) causes of health Which is a more distal health factor, smoking or neighborhood ● SDH­ social factors and the physical conditions in the environment in which people are born, live, learn, play, work, and age ● examples: social conditions ● exposure to crime, violence and social order; social support and interactions; socioeconomic conditions (concentrated poverty), quality schools, availability of resources and access to healthy foods, education/job opportunities, transportation options ● physical determinants: exposure to toxins, pollution), housing, neighborhoods, physical barriers (for people with disabilities), ● proximal (downstream) causes of health – behaviors (smoking, drinking, exercise) ● distal (upstream) causes of health –structure determinants (policies, political climate, economic structure) & social determinants (occupation, education, transportation, wealth/status, housing/neighborhood) 51. What was the significance of the Whitehall study Macaque monkey study Twin study How does power, control, and stress interplay to impact health ○ whitehall study ■ mid 60’s prior to office jobs ■ civil servants→ ■ more control, less stressful ■ 25% determined by behavior and 75% by social stress ● the more power and control someone has, the healthier they are because they feel more in control of themselves ● the more stressed out someone is, the less healthy they are 52. What is the “social gradient in health” or “health­wealth gradient” What are the two main hypotheses that explain the observed statistical correlation between social status and health ● inverse association between SES and morbidity/mortality/poor health; higher the SES, the better the health status and life expectancy ● Two main hypotheses ● social causation hypothesis: SES → Health ● lower SES associated w/ lower access to health care, higher stress, unhealthy behaviors, reduced access to health­ producing environments ● Health selection hypothesis: Health → SES ● “social drift” hypothesis: childhood health affects adult SES indirectly by influencing individual’s ability to achieve higher SES status (education) 53. What is a health disparity Describe the cultural/behavioral versus structural explanations for observed disparities in health. Which approach is better from a population health standpoint ● health disparity­ population­specific differences in morbidity, mortality, well­ being, HRQOL (health related quality of life), access to care, healthcare equality → observed AFTER CONTROLLING FOR BEHAVIOR ● differences in SES affect differences in distribution of resources (structural) which affect differences in behavior → differences in health outcomes ● need to focus on the structural view of disease since SES and social conditions affect access to resources (knowledge, power, social connectedness), which are beneficial in avoiding unhealthy behaviors and disease ● paying attention to behaviors is important also because it is proximal to health outcomes and morbidity/mortality 54. Would McKeown be considered a “behaviorist” or a “structuralist” ● McKeown would have been considered a “structuralist” because he believed that social, economic, and political conditions, not medical therapies, reduced mortality of infectious diseases in 1700s. Structuralist view of disease believes that SES is a fundamental cause of disease

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Chapter 7, Problem 3 is Solved
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Textbook: Control Systems Engineering
Edition: 7
Author: Norman J. Nise
ISBN: 9781118170519

Control Systems Engineering was written by and is associated to the ISBN: 9781118170519. This textbook survival guide was created for the textbook: Control Systems Engineering, edition: 7. This full solution covers the following key subjects: Error, feedback, figure, Find, Input. This expansive textbook survival guide covers 13 chapters, and 734 solutions. Since the solution to 3 from 7 chapter was answered, more than 268 students have viewed the full step-by-step answer. The answer to “For the unity feedback system shown in Figure P7.1, where G s 60 s 3 s 4 s 8 s2 s 6 s 17 find the steady-state error if the input is 80t 2u t . [Section: 7.2]” is broken down into a number of easy to follow steps, and 38 words. The full step-by-step solution to problem: 3 from chapter: 7 was answered by , our top Engineering and Tech solution expert on 11/23/17, 05:05AM.

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Solved: For the unity feedback system shown in Figure