NUR 550: Translational Research and Population Health Management Paper NUR 550: Translational Research and Population Health Management Paper Population health is the aggregation of various approach to health care that determines the health outcome of a group of individuals (Nash, JoAnne, Fabius, & Pracilio, 2011). Population health brings together the total quality of health of individuals in the community, considering the disparities in cultures, socioeconomical status, demographics, etc. The outcome of health of individuals in a giving population is highly determined by the policies that govern the healthcare delivery and care interventions (Nash et al., 2011). The care interventions include health screening, promotion and prevention, disease management, and chronic care management (Nash et al., 2011). To improve safety and and eliminate health disparities in the population, it is important to improve the quality of care of individuals, and the community as a whole by creating awareness about disease, providing education and setting in place facilities to help treat such diseases early before it becomes chronic illness. These are all categorized under primary, secondary, and tertiary intervention. According to Kindig, & Stoddart (2003), the concept and measurement of health and health outcomes focuses attention and research effort on the impact of each determinant and their interactions on some appropriate outcome, and it also allows one to consider health inequality and inequity and the distribution of health across subpopulations, as well as the ethical and value considerations underpinning these issues. Nash et al. (2011), the basic attributes of population health as follows: Patient-centered care Identified care provider Interdiciplinary healthcare team members such as physical therapists, spech therapists, occupational therapists, social workers, etc Knowledge and recognition of determinants of health and the impact on individuals and the population Integration of the community systems with public health. NUR 550: Translational Research and Population Health Management Paper ORDER INSTRUCTION-COMPLIANT PAPERS HERE Application of evidence-based practice to provide good quality, and cost effective care provision of culturally and linguistically appropriate care and health education Implementation of interoperable cross-sector health information technology Nash et al. (2011) defines health disparities as ?difference in the incidence, prevalence, mortality, and the burden of?diseases, as well as other adverse health conditions or outcomes that exists among specific population groups, and have well-documents in subpopulations based on socioeconomic status, education, age, race and ethnicity, geography, disability, sexual orientation,, or special needs?. Disparities in healthcare can lead to high mortality and morbidity rates. It can also lead to low quality of life (Nash et al., 2011). It is important to recognize the impact that social determinants have on health outcomes of specific populations and strive to improve the health of all groups. Population health has opened the eyes of the government and other private sectors to the disparities in population health, and these bodies have come together to improve the healthcare system of the country. NUR 550: Translational Research and Population Health Management Paper Over the years, efforts to eliminate disparities and achieve health equity have focused primarily on diseases or illnesses and on health care services. During the past 2 decades, 1 of Healthy Peoples overarching goals has focused on disparities. In Healthy People 2000, it was to reduce health disparities among Americans. In Healthy People 2010, it was to eliminate, not just reduce, health disparities. In Healthy People 2020, that goal was expanded even further: to achieve health equity, eliminate disparities, and improve the health of all groups (Nash et al, 2011). Kindig, D., & Stoddart, G. (2003). What is population health? Am J Public Health. 93(3): 80-383 Nash, D., B., Reifsnyder, J., Fabius, R., J., & Pracilio, V. P. (2011). Population health: Creating a culture of wellness. Sudbury, MA: Jones & Bartlett Learning, LLC. NUR 550: Translational Research and Population Health Management Paper Epidemiological practice and the results of epidemiological analysis make a significant contribution to emerging population-based health management frameworks. Population-based health management encompasses the ability to: Assess the health states and health needs of a target population; Implement and evaluate interventions that are designed to improve the health of that population; and Efficiently and effectively provide care for members of that population in a way that is consistent with the communitys cultural, policy and health resource values. Modern population-based health management is complex, requiring a multiple set of skills (medical, political, technological, mathematical etc.) of which epidemiological practice and analysis is a core component, that is unified with management science to provide efficient and effective health care and health guidance to a population. This task requires the forward looking ability of modern risk management approaches that transform health risk factors, incidence, prevalence and mortality statistics (derived from epidemiological analysis) into management metrics that not only guide how a health system responds to current population health issues, but also how a health system can be managed to better respond to future potential population health issues. Examples of organizations that use population-based health management that leverage the work and results of epidemiological practice include Canadian Strategy for Cancer Control, Health Canada Tobacco Control Programs, Rick Hansen Foundation, Canadian Tobacco Control Research Initiative.NUR 550: Translational Research and Population Health Management Paper 58We findthata health extending intervention might producemorerather than lessDALY.This happens when the yearsof life added adjusted for their quality,kh*,aremorethan compensated by the shift in reference age fromT() toT(+k).Figure 5reports results fork=1 andk=30. Note that these graphs are different from the previousones andeach curvenow correspondsto different values ofh*.Figure5Ratio of reduction in disability to gain in health ()?empirical estimatesusing recent, English life tables. Thex-axis reports the age at which death is prevented().values are reported for intervention extending life by1year in the left-hand sidegraph and by 30 years in the right-hand side graph with a quality of lifeh*. The curvesforh*=1 andh=0.1 are indicated to guide the reader.3.3DiscussionA health-utilitarian and a disability utilitarian health planner would rankinterventions in a systematically different way, even if both made the same assumptionsabout costs and effectiveness, and assumed consistent health and disability weights, solong as the form of the DALY used embodies a death-dependent conceptof thereference age.Considerfor examplea decision maker who can fund treatment for one and onlyone of the following people:(i)a 65 year old man who, untreated, will die today;(ii)a 45. NUR 550: Translational Research and Population Health Management Paper 59year old man who, untreated, will die today.The person receiving thetreatment will livefor one otheryear,with quality of life valued at 0.1 on a 0 to 1 scale where, as usual,thequality of life associated with being dead is 0 and that associated with perfect health is1. Let us assume that the decision maker wants tomaximise the health benefit,measured as gains in QALYs or reduction in DALYs.If the decision maker measures the health benefit with a QALY metric, funding anyof the two interventions would lead to a gain of 0.1 QALY. This endorses an egalitarianjudgment that QALY are equal, no matter who receives themand the decision makermight set up a lottery to determine who will receive the intervention or invoke furtherdecision criteria, e.g. to favour younger over older patients on afair-inningsargument(Williams 1997).On the other hand, if the decision maker measures the health benefit using a DALYmetric, funding the first intervention would lead to an increasein the burden of diseaseof 0.16 DALYs, but funding the second interventionwould leadto a slight reduction inthe burden of disease of 0.02 DALYs,and would then offer the treatment to the 45 year old man. In fact, she would not provide the intervention to the 65 year old man even ifresources were available to fund it, because his death today is associated with a lowerburden of disease than his death in a years time, which is at variance with the originalassumption that a quality of life of 0.1 is better than death.We find the DALY valuation of a health benefit quite problematic when computed inthis wayand that it is difficult to see how an intervention which increases an oldpersons life (even at a level of health only marginally better than being dead) canrepresent anincreasein the burden of disease.This difference between QALY-and DALY-based rankings of interventions, however,is not determined by the use of a disability perspective per se. In particular, ifthegovernment planners preferences meet the condition for health measurements to exist,and there is an upper bound on all possible lifetimes, then disability measurements alsoexist, asD(?)can be found byusing a consistent set of health and disability weight,d(a) =1-h(a),and setting the reference age to a constant value ofT??.Under theseconditions,H()+D(), is equal toT. However, sinceH()+D()=H()+D()=T, by simple. NUR 550: Translational Research and Population Health Management Paper 60algebra, any increase in health must be matched with an identical decrease in disability,i.e. ΔH(,)=-ΔD(,).To extend this result to measures of DALY averted at the population level, theselected constant reference ageTshould be identical for all individuals. For theequivalence of the two approaches to hold, operationally, the reference agesimplyneeds to be higher than any admissible age for a human life.We think that the language of disability measurement is a useful one, particularlywhen introducing health measurement concepts to professionals trained in publichealth or epidemiology, to whom years of life lost represent a natural intellectualstarting point.We are not suggesting the use of a constant reference age for the DALY approach ingeneral. If one is interested in describing the health status of a population in terms ofthe current, total burden of disease, the use of life expectancy from a standard, ideal orlocal populationshould be usedas recommended in the DALY framework(Murray 1996,Murray and Acharya 1997).If one is interested in the benefits from an intervention,however, one should use a health perspective with a QALY-type measure or a disabilityperspective with a constant reference age.This is however simply an algebraic fix toavoid erroneous estimates and misleading recommendations of using DALY-typemeasures to assess benefits from health interventions.3.4AppendixIn this appendixwediscuss when 0??T(+k)-T()??k. We do this in two steps. First,wedefine anddiscuss the shape of the residual life expectancyfunctionL(x),identifyingthe conditions under which its first derivative lies between-1 and 0. Then, weshow thatT(+k)-T()is always positive and discuss whenT(+k)-T()??k.Let us define the following three functions(Keyfitz 1968,Lindsey 2004):(1)thesurvivor function,S(x), that is the probability of living until agex:(1)?????xdttfxFxTxS)()(1]Pr[)(; NUR 550: Translational Research and Population Health Management Paper 61whereF(x)is the cumulative distribution andf(x)is the correspondingdensity function;(2)themortality rate,(x),that is the instantaneous probability that death willoccur at agex:(2))()()(xSxfx?;(3)theresidual life expectancy, L(x),that is the average prospective lifetime of anindividual agedx:(3))()()(xSdttSxLx?;DifferentiatingL(x)with respect tox:(4))()(1)())((22xLxSdttSxfSdxdLx???????;It can be easily seen thatdL/dx??-1 always, because both(x)andL(x)are nonnegative; and dL/dx??0if and only if(x)?L(x)??1, that is residual life expectancy is adecreasing function in correspondence of agesxwhere)(1)(xxL.Empirical analysis of life tables shows thatL(x)may increase during early years oflife, when there is a high risk of infant mortality. In developed countries, where the lifeexpectancy at birth is above 70 years, this usually happens only for the first year of lifeor even just for the first few months, andL(x)is a decreasing function of agexthereafter(Coale and Demeny 1983,Goldman and Lord 1986,Shrestha 2005).Let us now discuss when 0??T(+k)-T()?? k.First note thatT()=+L().We can re-writeT(+k)-T()=k+L(+k)-L() asT(+k)-T()=???kLkLk)()(1= NUR 550: Translational Research and Population Health Management Paper 62=???????????????kLLkkLkLkkLkLk)()1(?)2()1()1()(1.The years gained with the intervention,k, are non-negative. As we discussed above,the first derivative ofL(x)is greater than-1 for anyx, hence the term in square bracketsis non negative, that isT(+k)-T()??0 always.Similarly, for values ofxwhere the firstderivativedL/dx??0, that is when()?L()??1, the term in square brackets is less thanone, henceT(+k)-T()?? k. NUR 550: Translational Research and Population Health Management Paper 634Requisite models for strategic commissioning: the example of type1 diabetesThis chapter has been published as: M Airoldi, G Bevan, A Morton, M Oliveira, JSmith (2008) Requisite models for strategic commissioning: the example of type 1diabetes, Health Care Management Science, 11: 89-110AbstractA developing emphasis of healthcare reforms has been creatingorganisations with responsibilities for strategic commissioning of servicesfor defined populations. Such organisations must set priorities in aiming tomeet their populations needs subject to a budget constraint. This requiresestimates of the health benefits and costs of different interventions fortheir populations. This paper outlines a framework that does this and showshow this requires modelling to produce estimates in a way that istransparent to commissioners, ofrequisite complexity to produce soundestimates for priority setting using routinely available data. The exampleillustrated in this paper is an intervention that would improve glucosecontrol in the English population with type 1 diabetes. It takes manyyearsfor a change in glucose management to deliver maximum benefits; hencethe intervention is not good value-for-money in the short run. We aim togive a more strategic view of the costs and benefits modelling costs andbenefits in a steady-state model which suggests that the intervention isgood value-for-money in the long run.4.1IntroductionCost-effectiveness analysis (CEA) and disease modelling have grown apace in thehope of informing policy formation, however many authors have questioned their actualcontribution to the development and implementation of policies(Ross 1995,Drummond, Cooke et al. 1997,Duthie, Truemanet al. 1999,Drummond and Weatherly2000,Bryan, Williams et al. 2007). This paper develops a framework for CEA and cost- NUR 550: Translational Research and Population Health Management Paper 64effectiveness analysis to provide information for organisations responsible for strategiccommissioning of health services for defined populations and illustrates its use bymodelling intensive glucose control in type 1 diabetes in England. Strategiccommissioners (or purchasers) have emerged in reforms of health care, which arerequired to assess needs of populations, determine the optimal way of meeting theseneeds, and accordingly contract with providers of different services. This is currently thetask of Primary Care Trusts (PCTs) in the National Health Service (NHS) in England(Department of Health 2006)and Local Health IntegrationNetworks (LHINs) in Ontario(Ontario 2006). The second section of this paper outlines the framework we havedeveloped tohelp strategic commissioners set priorities. The third section illustrateshow this framework was used in modelling type 1 diabetes. The final section discussesthe results and implications of our framework for disease modelling.4.2Framework of analysisThe mainstream evaluation framework in economic evaluation for priority setting isthat of Quality-Adjusted Life Years ((Weinstein and Stason 1977,Williams 1985); see(Gold, Siegel et al. 1996,Drummond, Sculpher et al.2005)for a review of proposed,albeit less widespread alternatives). A Quality-Adjusted Life Year (QALY) is a yearweighted for quality of life, with a weight of one for perfect health and zero for death.QALYs are used to compare alternative interventions and to prioritize cost-effectiveinterventions for funding. The cost-effectiveness of an intervention is measured by theratio between its added value in terms of health benefits and its incremental costcompared to an alternative, the ?incrementalcost-effectiveness ratio? or simply?cost/QALY?. Interventions with a lower cost/QALY represent better value for moneybecause a smaller investment is needed to produce a unit of benefit or, alternatively,more QALYs can be achieved per unit spent. A different measurement tool that raised aheated debate is the concept of Disability-Adjusted Life Years (DALYs) to estimate theBurden of Disease (BoD) in a population(Anand and Hanson 1997,Murray and Acharya1997,Williams 1999,Mooney and Wiseman 2000,Murray and Lopez 2000,Bevan andHollinghurst 2003). DALYs are a form of summary measures of population health andcombine information on mortality and morbidity (for a review of alternative measuresseeLopez(2002)) and consist of the sum of Years of Life Lost (YLLs) from prematuremortality and Years Lived with a Disability (YLDs), in which each year of life is weighted. NUR 550: Translational Research and Population Health Management Paper 65for disability witha weight of zero for perfect health and one for death. Thesedifferent approaches have subsequently been developed to converge to produceinformation on costs and benefits of interventions in the population in terms ofreductions in BoD measured in DALYs(Hutubessy, Chisholm et al. 2003,Andrews,Issakidis et al. 2004,Evans, Edejer et al. 2005), or gains in health, measuredin QALYs(Dawson, Gravelle et al. 2005,Department of Health 2005,Martin and Smith 2006,UKCentre for the Measurement of Government Activity 2006).Besidecommon serious methodological, ethical and empirical problems(Gold,Siegel et al. 1996,Lopez, Mathers et al. 2002), each approach, as originally developed,was subject to different limitations as bases for setting priorities. The methodology ofCost/QALY was designed for marginal analysis: it does not distinguish interventions oflow cost and low benefit from those of high cost and high benefit; does not tell uswhether the bulk of resources are being currently used effectively(Hutubessy,Chrisholm et al. 2003,Evans, Adam et al. 2005); nor the number of people affectedbyan intervention. The value of reporting on the scale of the intervention has beenhighlighted by Murray and Lopez(Murray and Lopez 2000): ?If there are fixed assets,otherthan disposable dollars, limiting the feasible combinations of interventions thatcan be delivered?real-world examples include the attention of senior Ministry ofHealth decision-makers or the political commitment of government leaders?, thenthese should be devoted not just to the most cost-effective interventions but to thosecost-effective interventions with the potential to effect substantial improvements inpopulation health status.The standard approach to estimatingBoD in DALYs, however,givesestimates of that which exists given the current delivery of health care, and henceis best described as the ?current BoD. Estimates ofthecurrent BoD in DALYsare of novalue in themselves, nor a good guide on the potential benefit from an intervention.NUR 550: Translational Research and Population Health Management Paper. Hollinghurstet al.(2000)estimate the current BoD and the potential benefits frominterventions inthe South West of England. Estimates varied greatly across differentdiseasesand showed that,although the current BoD of heart diseases was higher thanthat of depression, the DALYs that are potentially avoidable by improving treatment ofdepression were much more than those of improving treatment of heart diseases.Toset priorities using DALYs, we require information on benefits and costs, but to interpretthe relationship between DALYs and costs, we need to distinguish between estimates ofthree different components of BoD(Bevan, Hollinghurst et al. 1998,Hollinghurst, Bevan. NUR 550: Translational Research and Population Health Management Paper ORDER INSTRUCTION-COMPLIANT PAPERS HERE In 2009 unspecified electronic survey was conducted with factors such as type, frequency, perpetrators, and professional/personal concerns on bullying identified (Quine, 2001). The results revealed that out of 330 RN respondents, 72% reported positive to bullying at various occasions in line of their career. Of this segment, clear hostility seemed most frequent in surgical/medical, operational rooms, emergency, obstetrical areas of care and adolescent residential behavioral/ mental health units. The main culprits to these act are non-other than; charge nurses, senior nurses, physicians and nurse managers.?NUR 550: Translational Research and Population Health Management Paper Individual assaults might be through criticizing, belittling, and intimidation, isolation, degradation and malice. For instance, one way of bullying could be a case where managers can ask their staffs to perform duties which they well know are beyond the staffs ability since they have no knowledge of what to be done on such duties. Furthermore, the managers may not pay attention when the employees bully each and at the same time they may take advantage of the bullying as way of improving and managing their organization. Additional nurse types of bullying include: o The super nurse ? the nurse that shows how powerful they are o he resentful nurse ? the nurse who hates others o The put down gossip and rumors nurse ? the nurse that speaks ill about others o The backstabbing nurse ? the nurse who pretend to have others back yet they are against them o The cliquish nurse ? the nurse who isolates themselves o The green with envy nurse o ? the nurse who snobs others and tends to favor others (Dellasega, 2009). In most cases, no one enjoys to be bulled but it happens and one has to find a way of dealing with. Besides, Bullying is not too far from harassment.it may differ somehow since harassment might be influenced by differences in race gender biasness, age In 2009 unspecified electronic survey was conducted with factors such as type, frequency, perpetrators, and professional/personal concerns on bullying identified (Quine, 2001). The results revealed that out of 330 RN respondents, 72% reported positive to bullying at various occasions in line of their career. Of this segment, clear hostility seemed most frequent in surgical/medical, operational rooms, emergency, obstetrical areas of care and adolescent residential behavioral/ mental health units. NUR 550: Translational Research and Population Health Management Paper. The main culprits to these act are non-other than; charge nurses, senior nurses, physicians and nurse managers.?NUR 550: Translational Research and Population Health Management Paper Individual assaults might be through criticizing, belittling, and intimidation, isolation, degradation and malice. For instance, one way of bullying could be a case where managers can ask their staffs to perform duties which they well know are beyond the staffs ability since they have no knowledge of what to be done on such duties. Furthermore, the managers may not pay attention when the employees bully each and at the same time they may take advantage of the bullying as way of improving and managing their organization. Additional nurse types of bullying include: o The super nurse ? the nurse that shows how powerful they are o he resentful nurse ? the nurse who hates others o The put down gossip and rumors nurse ? the nurse that speaks ill about others o The backstabbing nurse ? the nurse who pretend to have others back yet they are against them o The cliquish nurse ? the nurse who isolates themselves o The green with envy nurse o ? the nurse who snobs others and tends to favor others (Dellasega, 2009). In most cases, no one enjoys to be bulled but it happens and one has to find a way of dealing with. Besides, Bullying is not too far from harassment.it may differ somehow since harassment might be influenced by differences in race gender biasness, age.?NUR 550: Translational Research and Population Health Management Paper Order Now

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Translational Research and Population Health Management

Introduction

Translational research is the study of the process by which new knowledge, skills and technologies are translated into improved health outcomes for populations. Population health management (PHM) is a multidisciplinary approach to improving population health using data across many domains.

What is translational research?

Translational research is the process of translating scientific discoveries from the laboratory to the clinic. It is a way of making discoveries that can be used in clinical practice, and it involves multiple steps, including basic research and clinical application. The translational process starts with basic research, which generates new knowledge about disease processes or other health issues; this information is then tested in an animal model before being translated into human trials. Once these studies have been completed successfully on animals (and if they pass ethical standards), other scientists must carry out further testing on humans to determine if any changes have occurred as a result against what was expected before conducting this new research path forward onto us!

Translational medicine helps physicians understand how different drugs work on various systems within our bodies because each individual has unique genetic profiles that influence their response towards medications or other therapies administered through injections as well as inhalation methods such as nebulizers or inhalers. Translational medicine is also useful for understanding how the body responds to treatments for different types of cancer, since each type has its own unique characteristics and causes.

What is population health management?

Population health management is the use of data and technology to improve the health of populations. It combines medicine, policy and research in an effort to improve population health outcomes, reduce costs and improve population health.

The specifics vary from community to community, but there are three key components of PHM: 1) identifying at-risk populations; 2) preventing or delaying problems before they occur; 3) improving existing services.

What are the challenges in implementing translational research in PHM?

• Translational research is not always easy to implement. It can be expensive and time consuming, requiring significant resources such as human resources and infrastructure.

• Translational research is difficult to scale up—it requires funding from multiple sources without which the project would fail (e.g., public or private sector).

• Translational research does not always gain approval from stakeholders such as patients or doctors if it involves new treatments with unknown outcomes that may affect their health status negatively instead of positively affecting their lives positively

The combination of translational research and PHM can be very powerful

The combination of translational research and population health management can be very powerful. For example, you can use translational research to identify health issues that need further study, such as a specific group of people who have been diagnosed with a disease or condition. You can also use population health management to identify populations of people who should be studied in order to better understand their health status and needs.

Combining this data in a way that leads to more discoveries is something we hope will happen more often as the field matures over time!

Conclusion

In conclusion, the combination of translational research and population health management can be very powerful. This is because it enables organizations to generate more information about what works for different populations and then implement these changes in programs that are most effective at reaching those goals.