Most Americans are concerned about the "crisis in health care." Are older adults responsible for

Most Americans are concerned about the "crisis in health...

Key Concepts

Cost Drivers in Health Care

Cost drivers in health care encompass the various elements that contribute to rising expenditures in the industry. These drivers include factors such as technological advancements, higher prices for medical procedures and services, administrative expenses, and the management of chronic diseases. Recognizing these drivers helps explain cost increases without placing disproportionate blame on any single factor like the demographics of older adults.

Health Care Expenditures

This concept involves the total spending on health services and products within a system, including costs associated with hospitals, physicians, prescription drugs, and administrative processes. Understanding health care expenditures requires looking at a wide range of factors beyond individual population groups, such as technology, fee structures, and overall demand for services.

Aging Population

The aging population refers to the increasing number of older individuals within society who typically require more frequent and intensive health care services due to chronic conditions and age-related health issues. While this group can have higher per-capita spending, it is important to consider their health care needs in the context of overall demographic trends and the broader system requirements.

Policy and Insurance Systems

This concept refers to the frameworks established by government policies and insurance models that regulate how health care is delivered and financed. These systems influence pricing, coverage, and resource allocation within the health care market. Their design and reforms play a critical role in shaping overall spending patterns and can either mitigate or exacerbate cost pressures.

Transcript

00:01 This question talks about two identical surveys that were taken a month apart and asks if there is a significant difference between them.

00:09 The first, well, so first what we need is a null hypothesis.

00:13 This is going to be a hypothesis test, and so we need a null hypothesis to test against.

00:20 The null is going to be that nothing changed.

00:23 That's what we don't have any evidence to suggest that something did change.

00:27 So a skeptic would believe that nothing probably changed.

00:31 So we're just going to stick with the same proportions.

00:36 So that means that our null hypothesis will be in the new survey, the survey that was taken in october, the proportions will be the same as what was taken in september.

00:48 So to write this out, we'll have that the probability of someone saying that health care requirements are getting better after the bill will be 0 .22.

01:00 Probability of people saying that they will not share change, p of n, i'll call it, is going to be 0 .35.

01:08 A proportion of people saying that they'll get worse, p of w is going to be 0 .38, and the proportion of people who are unsure will be 0 .05, 5%.

01:22 So this is our null hypothesis.

01:24 And so let's do a hypothesis test on this.

01:27 We know that this is a multinomial experiment.

01:30 We have four categories.

01:31 So we're going to be using a kai squared test statistic.

01:35 For kai squared, of course, we know we need observed and expected values.

01:41 We'll be able to figure out our expected values, but let's take a look first at our observed values.

01:45 The observed values are going to be the values that we found in the survey taken in october.

01:50 So this is the survey that we're testing.

01:53 We found that 380 said that they would get better.

01:58 380 also said that they would stay the same, no change.

02:01 700 say that they would get worse.

02:03 And 61 said that they would not.

02:06 They were unsure.

02:08 So these are our observed values.

02:09 Now, what are our expected values? well, the expected values are going to be the null probabilities, what we just wrote up here, times the sample size.

02:20 And here the sample size is 1 ,521.

02:24 That's how many people were surveyed in the october survey.

02:28 So with 1 ,521 people, we'd multiply that by each.

02:34 Of our null probabilities and we'll get the our expected values.

02:39 So i'm going to do that now.

02:41 0 .22 times 1521 is going to be 3334 .62.

02:50 0 .35 times 1521 is going to be 532 .35.

03:00 We'll have 38 % of 1521, which is 577.

03:06 0 .98 and we'll have 5 % of 1 ,521, which is just 226, no, excuse me, 76, getting ahead of myself, 76 .5.

03:23 So now we need to find the difference, and we need to find not just the difference, the difference, squared.

03:30 So we're going to find o minus e squared because this is a part of our kai squared statistic.

03:37 So i'm going to do this actually just in a calculator.