Question

In 4 to 5 paragraphs, answer the questions below, which mirror the critical elements for Section IV of the final project. They will help you complete this section of your final project.

IV. Risk: In this section, you will identify the population at risk, discuss incidence and prevalence, interpret data, and discuss treatment options that exist for the communicable disease.

1. Have you identified the population that is at risk of developing or contracting the disease, explaining why that specific population is at risk?
2. Have you determined the current incidence and prevalence of the disease in the at-risk population, and have you supported your determination with current research?
3. Have you selected a graph that accurately displays the national incidence of the disease over time? Have you chosen a graph that includes at least five years of data?
4. Have you described the direction of the trend on the graph, explaining why the trend looks the way it does? Have you supported your explanation with current research?

Answer 1

Morbidity has been defined as any departure, subjective or objective, from a state of physiological or psychological well-being. In practice, morbidity encompasses disease, injury, and disability. In addition, although for this lesson the term refers to the number of persons who are ill, it can also be used to describe the periods of illness that these persons experienced, or the duration of these illnesses.(4)

Measures of morbidity frequency characterize the number of persons in a population who become ill (incidence) or are ill at a given time (prevalence).

Incidence refers to the occurrence of new cases of disease or injury in a population over a specified period of time. Although some epidemiologists use incidence to mean the number of new cases in a community, others use incidence to mean the number of new cases per unit of population.

Two types of incidence are commonly used — incidence proportion and incidence rate.

Definition of incidence proportion

Synonyms for incidence proportion

Attack rateRiskProbability of developing diseaseCumulative incidence

Incidence proportion is the proportion of an initially disease-free population that develops disease, becomes injured, or dies during a specified (usually limited) period of time. Synonyms include attack rate, risk, probability of getting disease, and cumulative incidence. Incidence proportion is a proportion because the persons in the numerator, those who develop disease, are all included in the denominator (the entire population).

A POPULATION PERSPECTIVE

For nations to improve the health of their populations, some have cogently argued, they need to move beyond clinical interventions with high-risk groups. This concept was best articulated by Rose (1992), who noted that “medical thinking has been largely concerned with the needs of sick individuals.” Although this reflects an important mission for medicine and health care, it is a limited one that does little to prevent people from becoming sick in the first place, and it typically has disregarded issues related to disparities in access to and quality of preventive and treatment services. Personal health care is only one, and perhaps the least powerful, of several types of determinants of health, among which are also included genetic, behavioral, social, and environmental factors (IOM, 2000; McGinnis et al., 2002). To modify these, the nation and the intersectoral public health system must identify and exploit the full potential of new options and strategies for health policy and action.

Three realities are central to the development of effective population-based prevention strategies. First, disease risk is currently conceived of as a continuum rather than a dichotomy. There is no clear division between risk for disease and no risk for disease with regard to levels of blood pressure, cholesterol, alcohol consumption, tobacco consumption, physical activity, diet and weight, lead exposure, and other risk factors. In fact, recommended cutoff points for management or treatment of many of these risk factors have changed dramatically and in a downward direction over time (e.g., guidelines for control of “hypertension” and cholesterol), in acknowledgment of the increased risk associated with common moderately elevated levels of a given risk factor. This continuum of risk is also apparent for many social and environmental conditions as well (e.g., socioeconomic status, social isolation, work stress, and environmental exposures). Any population model of prevention should be built on the recognition that there are degrees of risk rather than just two extremes of exposure (i.e., risk and no risk).

The second reality is that most often only a small percentage of any population is at the extremes of high or low risk. The majority of people fall in the middle of the distribution of risk. Rose (1981, 1992) observed that exposure of a large number of people to a small risk can yield a more absolute number of cases of a condition than exposure of a small number of people to a high risk. This relationship argues for the development of strategies that focus on the modification of risk for the entire population rather than for specific high-risk individuals. Rose (1981) termed the preventive approach the “prevention paradox” because it brings large benefits to the community but offers little to each participating individual. In other words, such strategies would move the entire distribution of risk to lower levels to achieve maximal population gains.

The third reality, provided by Rose's (1992) population perspective, is that an individual's risk of illness cannot be considered in isolation from the disease risk for the population to which he or she belongs. Thus, someone in the United States is more likely to die prematurely from a heart attack than someone living in Japan, because the population distribution of high cholesterol in the United States as a whole is higher than the distribution in Japan (i.e., on a graph of the distribution of cholesterol levels in a population, the U.S. mean is shifted to the right of the Japanese mean). Applying the population perspective to a health measure means asking why a population has the existing distribution of a particular risk, in addition to asking why a particular individual got sick (Rose, 1992). This is critical, because the greatest improvements in a population's health are likely to derive from interventions based on the first question. Because the majority of cases of illness arise within the bulk of the population outside the extremes of risk, prevention strategies must be applicable to a broad base of the population. American society experienced this approach to disease prevention and health promotion in the early twentieth century, when measures were taken to promote sanitation and food and water safety (CDC, 1999b), and in more recent policies on seat belt use, unleaded gasoline, vaccination, and water fluoridation, some of which are discussed later in this chapter.

The committee recognizes that achieving the goal of improving population health requires balancing of the strategies aimed at shifting the distribution of risk with other approaches. The committee does, however, endorse a much wider examination, and ultimately the development, of new population-based strategies. Three graphs illustrate different models for risk reduction .

These hypothetical models assume etiological links exist among all exposures and disease outcomes. Shows the effects of an intervention aimed at reducing the risk of those in the highest-risk category. In this example, people with the highest body mass index (BMI)1 are at in creased risk for cardiovascular heart disease and a plethora of chronic illnesses. Intervening medically, for example, to decrease risk (by lowering levels of obesity, as measured by BMI) ultimately decreases the proportion of the population with the highest BMIs. Such measures among very high-risk individuals may even be endorsed in cases where the “intervention” itself carries a substantial risk of poor outcome or side effects. However, use of such an intervention would be acceptable only in those whose medical risk was very high. Moreover, interventions in high-risk groups may have a limited effect on population outcomes because the greater proportion of those with moderate risk levels may ultimately translate into more chronic disease or other poor health outcomes.

illustrates Rose's classic model whereby the greatest benefit is achieved by shifting the entire distribution of risk to a lower level of risk. Because most people are in categories of moderately elevated risk as opposed to very high risk, this strategy offers the greatest benefit in terms of population-attributable risk, assuming that the intervention itself carries little or no risk. The hypothetical example shows what might occur if social policies or other population-wide measures were adopted to promote small decreases in weight in the general population. The committee embraces this kind of model of disease prevention in the case of policies such as seat belt regulation and the reduction of lead levels in gasoline.

The final hypothetical model although not discussed by Rose explicitly, illustrates a reduction in the distributions of those at highest and lowest risk with no change in the distribution of those with a mean level of risk. This model is appropriate for illustrating phenomena relating to inequality, where redistribution of some good (e.g., income, education, housing, or health care) reduces inequality without necessarily changing the mean of the distribution of that good. One hypothetical example is the association between low income and poor health. In many cases, there is a curvilinear association between these goods and health outcomes, with decreased health gains experienced by those at the upper bounds of the distribution. For example, data on income suggest that there are large differences in the health gains achieved per dollar earned for those at the lower end of the income distribution and fewer differences in the health gains achieved per dollar earned for those at the upper end. Thus, the curvilinear association, if it were a causal one, would suggest that substantial gains in population-level health outcomes may be achieved by a redistribution of some resources without actual changes in the means.

These graphs help to illustrate three different strategies for improving the health of the population. The nation has often endorsed the first strategy without a critical examination of the other two, especially the second one. The American public has grown accustomed to seeing differences in exposures to risk, both environmental and behavioral, and disparities in health outcomes. Acknowledging these gradients fully will help develop true population-based intervention strategies and help the partners who collaborate to assure the public's health move to take effective actions and make effective policies.

Understanding and ultimately improving a population's health rest not only on understanding this population perspective but also on understanding the ecology of health and the interconnectedness of the biological, behavioral, physical, and socioenvironmental domains. In some ways, conventional public health models (e.g., the agent–host–environment triad) have long emphasized an ecological understanding of disease prevention. Enormous gains in the control and eradication of infectious diseases rested upon a deep understanding of the ecology of specific agents and the power of environmental interventions rather than individual or behavioral interventions to control disease. For example, in areas where sanitation and water purification are poor, individual behaviors, such as hand washing and boiling of water, are emphasized to reduce the spread of disease. However, when environmental controls become feasible, it is easy to move to a more “upstream”2 intervention (like municipal water purification) to improve health. The last several decades of research have resulted in a deeper understanding not only of the physical dimensions of the environment that are toxic but also of a broad range of related conditions in the social environment that are factors in creating poor health. These social determinants challenge the discipline of public health to more fully incorporate them.

Over the past decade, several models have been developed to illustrate the determinants of health and the ecological nature of health (e.g., see Dahlgren and Whitehead [1991], Evans and Stoddart [1990], and Appendix A). Many of these models have been developed in the United Kingdom, Canada, and Scandinavia, where population approaches have started to shape governmental and public health policies. The committee has built on the Dahlgren-Whitehead model—which also guided the Independent Inquiry into Inequalities in Health in the United Kingdom—modifying it to reflect special issues of relevance in the United States. serves as a useful heuristic to help us think about the multiple determinants of population health. It may, for instance, help to illustrate how the health sector, which includes governmental public health agencies and the health care delivery system, must work with other sectors of government such as education, labor, economic development, and agriculture to create “healthy” public policy. Furthermore, the governmental sector needs to work in partnership with nongovernmental sectors such as academia, the media, business, community-based organizations and communities themselves to create the intersectoral model of the public health system first alluded to in the 1988 Institute of Medicine (IOM) report and established in this report as critical to effective health action.

Adding Trend Lines to Graphs

A trend line (also called the line of best fit) is a line we add to a graph to show the general direction in which points seem to be going. Think of a "trend" as a pattern in math. Whatever shape you see on a graph or among a group of data points is a trend.

Three types 1. Liner trend graph

2. Non Liner trend graph .

3. No trend graph

By incerasing and decreasing this trend graph ploted .

Thank You.