Propensity-Score Matching (Psm). The Propensity Score Matching

This design is more often used by practitioners compared with researchers to examine the effectiveness of a specific intervention on an individual or group’s behaviors.

9. Which variable was least affected by the empowerment program? Provide a rationale for your answer. The experimental subjects’ mean depression score showed 0.64

According to the Centers for Disease Control (CDC), “health equity is achieved when every person has the opportunity to attain his or her full health potential and no one is disadvantaged from achieving this potential because of social position or other socially determined circumstances” (U.S. Department of Health and Human Services, 2015). Satcher (2010) reports that health inequities are “systematic, avoidable, and unjust” disparities (p. 6). He also states that the World Health Organization (WHO) concluded that social conditions are the most important determinant of a person’s health. Social conditions “determine access to health services and influence lifestyle choices” (Satcher, 2010, p. 6). These determinants must be addressed in order to reduce health inequity. Inequity can be

2. How does this documentary series illustrate the disadvantages of longitudinal panel studies, as discussed in Babbie (pp. 113-114)?

After reviewing the lecture, I believe that the PPACA will significantly affect Health Disparities in the United States. Based on the readings, the features for this plan include giving incentive to business owners to provide insurance coverage to their workers whether If by penalty if there are over 50 employees or by providing tax credit to those with less. The health system focused on collecting enhanced data based on race, ethnicity, sex, primarily language, and disability status to look for information to improve health care. The main goals of the PPACA is to expand coverage, control costs, and improve the health care delivery system. It reduces disparities in multiple ways. For example, for African Americans they are more likely

Health disparities in populations and population subgroups deal with differences in overall health and the spread of disease and death (Almgren, 2013). There are several characteristics of a population or subgroup that make them more vulnerable to disparities in health and healthcare. These include race, ethnicity, sex, age, education, income, employment, and geographic location among other characteristics. Many of which are linked to social inequality within communities. On the other hand, healthcare disparities include access to care, quality of care, equity, and health care outcomes (Almgren, 2013, p.243). Disparities in both categories can be explained by the social determinants of health that affect many people’s health status and include environmental factors present in communities (Patel & Rushefsky,2014). All these factors are interrelated and seem to affect minority and low-income groups more disproportionately. Meyer et.al. (2013), use the World Health Organizations explanation of social determinants of health as being “mostly responsible for health inequities—the unfair and avoidable differences in health status seen within and between countries” (p. 3). This explanation is applicable to communities and population groups within the U.S. as

1. What is the intervention being evaluated? What is the hypothesis for the intervention, and what theories or empirical research is used to support that initial hypothesis?

There are three categories that summarize health disparities in the U.S. The first is disparities that have a social or economic cause rather than a biological cause. An example of this would be that the death rates of black American men are 26 percent higher than that of white men. Also, the death rates of black American woman are 19 percent higher than that of white women. An explanation for this is because blacks have a lower socioeconomic status than white. The reason for this is because blacks are more likely than white to never graduate high school, or graduate high school but not go to college (Barr, 43). Therefore, those with low socioeconomic status can’t afford to go to the doctor which results in a health disparity. The second category

This program would cut about 43 percent or equivalent to 40,000 enrollment participants’ slots. The program, at the time, housed or serviced 104,000 enrollees and after the reduction it would bring that number down to 64,000 enrolled (Renz, 2011). The reason this was a more serious issue for administrators to resolve, was the demographics of the participants; Low Income Families. Families that are, at the time, in a disadvantage state and needed means to provide for basic necessities when it came to health coverage.

While several aspects of the program can be evaluated, given the newness of the program, many outcome shave not been evaluated. Additionally, some outcomes have yet to actually occur. Nonetheless,

The Oregon Health Insurance Experiment is a groundbreaking study on the expansion of healthcare for low-income adults, which includes a look at health care outcomes, use, well-being, and financial burden. The study uses an innovated unsystematic strategy in which to gauge the overall impact of Medicaid in America; while random well-ordered studies are preferred within scientific observations, it is nearly impossible in social research. The state of Oregon, in 2008, decided to use a lottery in which to select low income, uninsured individuals, in an effort to produce such a study. As such, The Oregon Health Insurance Experiment is the combined effort of scientific researchers, and the state of Oregon in an attempt to better understand the costs/benefits of expanding public health care.

A rigorous evaluation typically involves either an experimental design (like that used in randomized controlled trials) or a quasi-experimental design. In an experimental design, people are randomly assigned to either a treatment group, which participants in the program, or the control group, which does not. After the program is completed, the outcomes of these two (2) groups are compared. This type of research design helps ensure that any observed differences in outcomes between the two (2) groups are the result of the program and not other factors. Given that randomization is not always possible, a quasi-experimental design is sometimes used. In evaluations using this design, the program participants are compared to a group of people similar in many ways to the program participants. However, because a quasi-experimental design does not randomly assign participants to program and non-program groups, it is not as strong a design as the experimental approach. Because there may be unobserved differences between the two (2) groups of people who are being compared, this design does not allow program evaluators to conclude with the same certainty that the program itself was responsible for the impacts observed. Therefore, it would be conducive to try and conduct an experimental design if at all possible (Cooney, Huser, Small, & O’Connor,

the program had a 30% chance of recidivating, unlike 47% for those who did not participate

The dependent variables that are going to be measured on their responses to offer or reserving their rights over blood donation. Subjects part of the experiment and control group will still be confronted with a chance to influence or affect their engagement altruistically for others’ wellbeing or survival.

The findings for the study conducted by Judge et al. (2015) suggested three designs that appear