/*! This file is auto-generated */ .wp-block-button__link{color:#fff;background-color:#32373c;border-radius:9999px;box-shadow:none;text-decoration:none;padding:calc(.667em + 2px) calc(1.333em + 2px);font-size:1.125em}.wp-block-file__button{background:#32373c;color:#fff;text-decoration:none} Problem 3 Quitting Smoking and Risk for Ty... [FREE SOLUTION] | 91影视

91影视

Log out

Learning Materials

Features

Discover

Chapter 9: Problem 3

Quitting Smoking and Risk for Type 2 Diabetes. Researchers studied a group of 10,892 middle-aged adults over a period of nine years. They found that smokers who quit had a higher risk of diabetes within three years of quitting than either nonsmokers or continuing smokers. \({ }^{4}\) Does this show that stopping smoking causes the short-term risk for Type 2 diabetes to increase? (Weight gain has been shown to be a major risk factor for developing Type 2 diabetes and is often a side effect of quitting smoking. Smokers also often quit due to health reasons.) Based on this research, should you tell a middle-aged adult who smokes that stopping smoking can cause diabetes and advise him or her to continue smoking? Carefully explain your answers to both questions.

Short Answer

Expert verified
Quitting smoking shows correlation, not causation, with increased diabetes risk. Encourage quitting while managing weight.

Step by step solution

01

Understanding the Research Study

First, recognize what the researchers have done: they observed 10,892 middle-aged adults over nine years to study the effects of quitting smoking on diabetes risk. They noted that those who quit smoking had an increased risk of developing diabetes within three years compared to nonsmokers or those who continued smoking.
02

Identifying Possible Confounding Factors

Consider factors that might confuse the results of this study. For instance, weight gain often follows quitting smoking and is a known risk factor for diabetes. Additionally, those who quit might have been experiencing health issues that led them to quit, which also could contribute to an increased risk of diabetes.
03

Causation vs. Correlation

Understand the difference between causation and correlation. The study shows a correlation between quitting smoking and increased risk of diabetes but does not necessarily prove that quitting directly causes diabetes to increase. Other factors such as weight gain and pre-existing health conditions might play significant roles.
04

Evaluating Health Advice

Given the data and understanding of causation, advising someone to continue smoking due to the risk of diabetes is not appropriate. The health risks associated with smoking, like heart disease and cancer, generally outweigh the increased risk of diabetes due to factors like weight gain after quitting.
05

Formulating an Advice

Encourage quitting smoking with a focus on managing weight and monitoring health post-quitting. It's important to take comprehensive health approaches rather than focusing solely on the diabetes risk.

Unlock Step-by-Step Solutions & Ace Your Exams!

  • Full Textbook Solutions

    Get detailed explanations and key concepts

  • Unlimited Al creation

    Al flashcards, explanations, exams and more...

  • Ads-free access

    To over 500 millions flashcards

  • Money-back guarantee

    We refund you if you fail your exam.

Over 30 million students worldwide already upgrade their learning with 91影视!

Key Concepts

These are the key concepts you need to understand to accurately answer the question.

Causation vs. Correlation
When studying complex topics like smoking and health risks, understanding the difference between causation and correlation is key. Correlation means that two things appear to be connected, while causation shows that one thing actually causes the other to happen. For instance, the study discussed here found that people who quit smoking had a higher risk of developing diabetes. But this doesn't prove that quitting smoking causes diabetes.

It's possible that other factors, such as weight gain after quitting, might explain this connection. So, while there's a correlation between quitting smoking and developing diabetes, we can't say quitting smoking directly leads to diabetes. These concepts are essential in research because misinterpreting correlation as causation can lead to incorrect conclusions and inappropriate health advice.
Confounding Factors in Studies
Confounding factors can complicate the results of studies by introducing variables that were not initially considered. In the case of quitting smoking and diabetes risk, weight gain is a significant confounder. Many people who quit smoking gain weight, and weight gain is a known risk factor for type 2 diabetes.

Other confounding factors include the reasons why people decide to quit smoking. Often, individuals might quit due to emerging health concerns, which themselves can influence diabetes risk. As researchers or individuals interpreting such studies, it's crucial to identify these confounding factors to accurately interpret the results. By doing so, we can understand the real risk factors without mistakenly attributing effects to the incorrect causes.
Health Advice for Smokers
Based on the research findings, it's clear that advising someone to continue smoking to avoid diabetes is not a good idea. Smoking carries numerous health risks, such as lung cancer, heart disease, and respiratory issues, which generally overshadow the diabetes risk linked to weight gain after quitting.

Instead of discouraging quitting, health advice should focus on supporting individuals in managing their health as they quit smoking. Some helpful strategies include:
  • Helping individuals to manage weight through diet and exercise.
  • Offering counseling and support groups to aid in the quitting process.
  • Regularly monitoring health indicators, such as blood sugar levels, to catch any early signs of diabetes.
By considering all aspects of a person's health, we provide a more comprehensive approach that benefits overall well-being rather than just addressing one potential issue.

One App. One Place for Learning.

All the tools & learning materials you need for study success - in one app.

Get started for free

Most popular questions from this chapter

Does exposure to aircraft noise increase the risk of hospitalization for cardiovascular disease in older people ( \(\geq 65\) years) residing near airports? Selecting a random sample of approximately 650,000 Medicare claims, it was found that about 75,000 people had zip codes near airports, and the remaining 575,000 did not. The proportions of hospital admissions related to cardiovascular disease were computed for those with zip codes near airports and those who did not have zip codes near airports. A larger proportion of admissions for cardiovascular disease was found for older people living in zip codes near airports. Which of the following statements is correct? a. Since this is an observational study, living in a zip code near an airport may or may not be causing the increase in the proportions of admissions for cardiovascular disease. b. Because of the large sample sizes from each group, we can claim that living in a zip code near an airport is causing the increase in the proportion of admissions for cardiovascular disease. c. Because this is an experiment, although not a randomized experiment, we can still conclude that living in a zip code near an airport is causing the increase in the proportions of admissions for cardiovascular disease.

Athletes Take 0xygen. We often see players on the sidelines of a football game inhaling oxygen. Their coaches think this will speed their recovery. We might measure recovery from intense exertion as follows: Have a football player run 100 yards three times in quick succession. Then allow three minutes to rest before running 100 yards again. Time the final run. Describe the design of two experiments to investigate the effect of inhaling oxygen during the rest period. One of the experiments is to be a completely randomized design and the other a matched pairs design in which each athlete serves as their own control. Suppose you have 20 football players available as subjects. For both experiments, carry out the randomization of the 20 football players to treatments as required by the design.

Observation Versus Experiment. Researchers at the University of Pennsylvania found that patients who were divorced, separated, or widowed had approximately a \(40 \%\) greater chance of dying or developing a new functional disability in the first two years following cardiac surgery than their married peers. The data included 1576 subjects who underwent cardiac surgery, of whom \(65 \%\) were married; \(33 \%\) were divorced, separated, or widowed; and \(2 \%\) had never been married. The findings were reported to be statistically significant.26 a. Without reading any further details of this study, how do you know that this was an observational study? b. Suggest some variables that might differ between the subjects in the study who were married versus those who were divorced, separated, or widowed. Are any of these possible confounding variables? Explain. c. Summarize briefly the limitations of this study. Despite these limitations, explain why this study still furnishes useful information in formulating a recovery plan for those undergoing cardiac surgery.

Quick Randomizing. Here's a quick and easy way to randomize. You have 100 subjects: 50 adults under the age of 65 and 50 who are 65 or older. Toss a coin. If it's heads, assign all the adults under the age of 65 to the treatment group and all those 65 and over to the control group. If the coin comes up tails, assign all those 65 and over to treatment and all those under the age of 65 to the control group. This gives every individual subject a \(50-50\) chance of being assigned to treatment or control. Why isn't this a good way to randomly assign subjects to treatment groups?

.Treating Sinus Infections. Sinus infections are common, and doctors commonly treat them with antibiotics. Another treatment is to spray a steroid solution into the nose. A well-designed clinical trial found that these treatments, alone or in combination, do not reduce the severity or the length of sinus infections. \(\frac{34}{4}\) The clinical trial was a completely randomized experiment that assigned 240 patients at random among four treatments, as follows: a. The report of this study in the Journal of the American Medical Association describes it as a "double-blind, randomized, placebo-controlled factorial trial." "Factorial" means that the treatments are formed from more than one factor. What are the factors? What do "double-blind" and "placebo-controlled" mean? b. If the random assignment of patients to treatments did a good job of eliminating bias, possible lurking variables such as smoking history, asthma, and hay fever should be similar in all four groups. After recording and comparing many such variables, the investigators said that "all showed no significant difference between groups." Explain to someone who knows no statistics what "no significant difference" means. Does it mean that the presence of all these variables was exactly the same in all four treatment groups?
See all solutions

Recommended explanations on Math Textbooks

Logic and Functions

Read Explanation

Discrete Mathematics

Read Explanation

Pure Maths

Read Explanation

Theoretical and Mathematical Physics

Read Explanation

Probability and Statistics

Read Explanation

Decision Maths

Read Explanation
View all explanations

What do you think about this solution?

We value your feedback to improve our textbook solutions.

Study anywhere. Anytime. Across all devices.