Information

How do people estimate smoking's impact on their mortality?

How do people estimate smoking's impact on their mortality?


We are searching data for your request:

Forums and discussions:
Manuals and reference books:
Data from registers:
Wait the end of the search in all databases.
Upon completion, a link will appear to access the found materials.

I am working on a project that looks at how information about one's health affects smoking decisions. I have an economic background, and not familiar with the psychology literature. So my questions are:

  1. How accurate are people in estimating their mortality risk?
  2. How well can people estimate smoking's impact on their mortality ?
  3. How does the new information about health status change people's belief about their mortality and smoking's impact on their mortality?

"How accurate are people in estimating their mortality risk?" "How well can people estimate smoking's impact on their mortality?" (I grouped these.)

Not accurate at all. One problem with your question is that the accuracy of predicting/estimating risk is difficult to do without the actual outcome: if a smoker predicts he will get cancer, and does develop it, that's 100% accuracy. (See [1] and [2].)

Many studies show that smokers underestimate their relative risk of all smoking-related disorders (including cancer, heart disease, chronic lung infections, etc.) compared to non-smokers. Also, smokers believe they have a lower risk of developing lung cancer than the average smoker. This has been labeled by some as “unrealistic optimism”. [1][2]

Together, the accumulated data demonstrate convincingly that smokers have a very imperfect understanding of the risks of smoking and of risk statistics in general. Furthermore, regardless of what they may acknowledge about the risks faced by other smokers, they believe that their own risk is less. Given the accumulated evidence, the argument that people begin to smoke or continue to smoke with adequate knowledge of the potential risks appears indefensible. [2]

Smokers do react behaviorally to bad news. In one study of smokers who received annual spiral CT scans x 3, 48% of smokers quit after three abnormal scans; 28.0% quit after two abnormal scans; 24.2% quit after one abnormal scan (compared to 19.8% with no abnormal screens).[3] A larger, later study (the Danish Lung Cancer Screening Trial [DLCST]) confirmed that quit rates were higher and relapse rate lower among subjects with initial CT findings that necessitated a repeat scan 3 months later.[4]

Regarding other forms of "bad news", the effect was less pronounced:

Of the fifteen included studies, only two detected a significant effect of the intervention. Spirometry combined with an interpretation of the results in terms of 'lung age' had a significant effect in a single good quality trial but the evidence is not optimal. A trial of carotid plaque screening using ultrasound also detected a significant effect, but a second larger study of a similar feedback mechanism did not detect evidence of an effect. Only two pairs of studies were similar enough in terms of recruitment, setting, and intervention to allow meta-analyses; neither of these found evidence of an effect. Mixed quality evidence does not support the hypothesis that other types of biomedical risk assessment increase smoking cessation in comparison to standard treatment. There is insufficient evidence with which to evaluate the hypothesis that multiple types of assessment are more effective than single forms of assessment.[5]

I hope this is enough to get you started.

[1] Smokers' unrealistic optimism about their risk, N D Weinstein et. al., Tob Control 2005;14:55-59 doi:10.1136/tc.2004.008375
[2] Perceived risks of heart disease and cancer among cigarette smokers, Ayanian JZ, JAMA. 1999 Mar 17;281(11):1019-21.
[3] Relation between smoking cessation and receiving results from three annual spiral chest computed tomography scans for lung carcinoma screening, CO Townsend et. al., Cancer. 2005 May 15;103(10):2154-62.
[4] Effect of CT screening on smoking habits at 1-year follow-up in the Danish Lung Cancer Screening Trial (DLCST). Ashraf H et. al., Thorax. 2009 May;64(5):388-92.
[5] Biomedical risk assessment as an aid for smoking cessation. Bize R, et. al., Cochrane Database Syst Rev. 2012 Dec 12;12:CD004705.


The "Loneliness Epidemic"


Nearly one out of three older Americans now lives alone -- and the health effects are mounting, experts say.

Loneliness and social isolation can be as damaging to health as smoking 15 cigarettes a day, researchers warned in a recent webcast, and the problem is particularly acute among seniors, especially during holidays.

Two in five Americans report that they sometimes or always feel their social relationships are not meaningful, and one in five say they feel lonely or socially isolated. The lack of connection can have life threatening consequences, said Brigham Young University professor Julianne Holt-Lunstad, who testified before the U.S. Senate in April, 2017 that the problem is structural as well as psychological.

For example, the average household size in the U.S. has declined in the past decade, leading to a 10 percent increase in people living alone. According to the U.S. Census Bureau, over a quarter of the U.S. population -- and 28 percent of older adults -- now live by themselves.

The good news is that friendships reduce the risk of mortality or developing certain diseases and can speed recovery in those who fall ill. Moreover, simply reaching out to lonely people can jump-start the process of getting them to engage with neighbors and peers, according to Robin Caruso of CareMore Health, which operates in 8 states and the District of Columbia with a focus on Medicare patients. Her "Togetherness" initiative aims to combat "an epidemic of loneliness" among seniors through weekly phone calls, home visits and community programs.

The two were among presenters in a panel discussion hosted by the National Institute for Health Care Management -- a non-profit research arm of the health insurance industry.

Among key findings: An estimated $6.7 billion in annual federal spending is attributable to social isolation among older adults. Poor social relationships were associated with a 29 percent increase in risk of coronary heart disease and a 32 percent rise in the risk of stroke, studies have shown. Authorities expect the financial and public health impact of loneliness to increase as the nation's population ages. Source: CareMore Health


Common health conditions associated with ageing

Common conditions in older age include hearing loss, cataracts and refractive errors, back and neck pain and osteoarthritis, chronic obstructive pulmonary disease, diabetes, depression, and dementia. Furthermore, as people age, they are more likely to experience several conditions at the same time.

Older age is also characterized by the emergence of several complex health states that tend to occur only later in life and that do not fall into discrete disease categories. These are commonly called geriatric syndromes. They are often the consequence of multiple underlying factors and include frailty, urinary incontinence, falls, delirium and pressure ulcers.

Geriatric syndromes appear to be better predictors of death than the presence or number of specific diseases. Yet outside of countries that have developed geriatric medicine as a speciality, they are often overlooked in traditionally structured health services and in epidemiological research.

Factors influencing Healthy Ageing

Although some of the variations in older people&rsquos health are genetic, much is due to people&rsquos physical and social environments &ndash including their homes, neighbourhoods, and communities, as well as their personal characteristics &ndash such as their sex, ethnicity, or socioeconomic status.

These factors start to influence the ageing process at an early stage. The environments that people live in as children &ndash or even as developing foetuses &ndash combined with their personal characteristics, have long-term effects on how they age.

Environments also have an important influence on the development and maintenance of healthy behaviours. Maintaining healthy behaviours throughout life, particularly eating a balanced diet, engaging in regular physical activity, and refraining from tobacco use all contribute to reducing the risk of non-communicable diseases and improving physical and mental capacity.

Behaviours also remain important in older age. Strength training to maintain muscle mass and good nutrition can both help to preserve cognitive function, delay care dependency, and reverse frailty.

Supportive environments enable people to do what is important to them, despite losses in capacity. The availability of safe and accessible public buildings and transport, and environments that are easy to walk around are examples of supportive environments.

Challenges in responding to population ageing

Diversity in older age

There is no &lsquotypical&rsquo older person. Some 80-year-olds have physical and mental capacities similar to many 20-year-olds. Other people experience significant declines in physical and mental capacities at much younger ages. A comprehensive public health response must address this wide range of older people&rsquos experiences and needs.

Health inequities

The diversity seen in older age is not random. A large part arises from people&rsquos physical and social environments and the impact of these environments on their opportunities and health behaviour. The relationship we have with our environments is skewed by personal characteristics such as the family we were born into, our sex and our ethnicity, leading to inequalities in health. A significant proportion of the diversity in older age is due to the cumulative impact of these health inequities across the life course. Public health policy must be crafted to reduce, rather than reinforce, these inequities.

Outdated and ageist stereotypes

Older people are often assumed to be frail or dependent, and a burden to society. Public health, and society as a whole, need to address these and other ageist attitudes, which can lead to discrimination, affect the way policies are developed and the opportunities older people have to experience Healthy Ageing.

A rapidly changing world

Globalization, technological developments (e.g. in transport and communication), urbanization, migration and changing gender norms are influencing the lives of older people in direct and indirect ways. For example, although the number of surviving generations in a family has increased, today these generations are more likely than in the past to live separately. A public health response must take stock of these current and projected trends, and frame policies accordingly.

WHO&rsquos response

In accordance with a recent World Health Resolution (67/13), a comprehensive Global Strategy and Action Plan on Ageing and Health is being developed by WHO in consultation with Member States and other partners. The Strategy and Action Plan draws on the evidence of the World report on ageing and health and builds on existing activities to address 5 priority areas for action.


The "Loneliness Epidemic"


Nearly one out of three older Americans now lives alone -- and the health effects are mounting, experts say.

Loneliness and social isolation can be as damaging to health as smoking 15 cigarettes a day, researchers warned in a recent webcast, and the problem is particularly acute among seniors, especially during holidays.

Two in five Americans report that they sometimes or always feel their social relationships are not meaningful, and one in five say they feel lonely or socially isolated. The lack of connection can have life threatening consequences, said Brigham Young University professor Julianne Holt-Lunstad, who testified before the U.S. Senate in April, 2017 that the problem is structural as well as psychological.

For example, the average household size in the U.S. has declined in the past decade, leading to a 10 percent increase in people living alone. According to the U.S. Census Bureau, over a quarter of the U.S. population -- and 28 percent of older adults -- now live by themselves.

The good news is that friendships reduce the risk of mortality or developing certain diseases and can speed recovery in those who fall ill. Moreover, simply reaching out to lonely people can jump-start the process of getting them to engage with neighbors and peers, according to Robin Caruso of CareMore Health, which operates in 8 states and the District of Columbia with a focus on Medicare patients. Her "Togetherness" initiative aims to combat "an epidemic of loneliness" among seniors through weekly phone calls, home visits and community programs.

The two were among presenters in a panel discussion hosted by the National Institute for Health Care Management -- a non-profit research arm of the health insurance industry.

Among key findings: An estimated $6.7 billion in annual federal spending is attributable to social isolation among older adults. Poor social relationships were associated with a 29 percent increase in risk of coronary heart disease and a 32 percent rise in the risk of stroke, studies have shown. Authorities expect the financial and public health impact of loneliness to increase as the nation's population ages. Source: CareMore Health


Research on teen smoking cessation gains momentum

A new wave of research promises to clarify the types of interventions that best help adolescents quit smoking.

For decades, tobacco control researchers poured their efforts into helping adults quit and preventing teens from taking up the habit.

"The folk wisdom was that you're not going to get teen-agers to quit until they're a bit older, so why bother?" says Steve Sussman, PhD, a professor of preventive medicine and psychology at the University of Southern California.

As a result, there have historically been few randomly controlled studies examining what works and what doesn't in helping adolescents quit smoking.

Recently, however, tobacco control experts have reconsidered their focus on prevention, in part because adolescent smoking has increased since 1991, after having plateaued in the 1970s and declined slightly during the 1980s. That suggests that even the best prevention efforts, as they are currently deployed, aren't enough to stem the tide of teen-age smoking, many researchers believe.

Further, recent research has indicated that--contrary to what experts had long assumed--teen-agers can become dependent on tobacco even before they begin smoking on a daily basis, that most adolescent smokers continue smoking into adulthood and that many want to quit but are unable to do so. In 1999, 35 percent of high school seniors had smoked a cigarette in the past month, and 23 percent were daily smokers. About 40 percent of adolescent smokers report having unsuccessfully tried to quit in the past.

Responding to the increased need for a better understanding of how to help teen-agers quit smoking, in 1997 and 1998 the National Cancer Institute (NCI) issued requests for applications for research on the effectiveness of youth smoking-cessation programs. NCI now funds 16 major studies of smoking cessation in youth. Several other public and private organizations--including the National Institute on Drug Abuse, the National Institute of Child Health and Human Development, the National Institute of Dental and Craniofacial Research, and the Robert Wood Johnson Foundation--have followed suit.

Tobacco control experts hope the results of this new wave of research--expected to begin emerging in the coming months--will help clarify a wide range of questions about how best to treat adolescent smokers. For example:

How do parents and peers affect adolescents' efforts to quit smoking, and how can cessation programs capitalize on their influence?

What are the developmental factors that adolescent cessation programs must consider in order to be successful?

Are pharmacological treatments as effective for adolescent smokers as they are for adults?

What kinds of programs work for adolescent smokers who are very heavily dependent on nicotine or who have other substance abuse or psychiatric problems?

What's the best way to tailor cessation programs to smokeless tobacco users, whose tobacco addiction is unique?

Preliminary hints

Despite the scarcity, so far, of randomized studies on the subject, there are some clues to what kinds of programs best help young people stop smoking. In 1999, Sussman and colleagues reviewed 17 published tobacco cessation studies. Ten studies were single-group studies and seven were quasi-experimental or experimental studies that included control groups.

The review, published in the journal Substance Use and Misuse (Vol. 34, No. 1), indicated that on average, about 21 percent of teen-agers in cessation programs quit smoking--a number that dropped to 13 percent six months after smoking interventions. In comparison, the average naturally occurring quit rate, without intervention, appears to range from 0 to 11 percent. Of the six studies that reported smoking reduction among adolescents who didn't quit, four reported that study participants reduced their smoking by at least half.

More recently, at the request of a consortium of U.S. and Canadian health agencies, Sussman has completed an expanded review of 66 adolescent smoking-cessation studies, 37 of which included control groups and 29 of which did not. In most of the studies, most participants were white.

The studies encompassed a range of theoretical approaches, including cognitive behavioral and motivational programs, programs in which participants are rewarded for quitting smoking, supply reduction strategies such as tax increases on tobacco or restricting access to cigarettes, pharmacological therapies and "stages-of-change" approaches tailored to teen-agers' interest in quitting.

The review, not yet published, indicates that in studies that included control groups, about 7 percent of teen-agers in the control group quit smoking. In comparison, about 12 percent of young people in cessation programs quit smoking over an average of eight months.

That's encouraging, Sussman says, but he warns, "There's so much variation that everything has to be taken with a grain of salt." Indeed, he notes, programs' quit rates ranged from 0 percent to 41 percent at follow-up.

The programs that appear most effective are those that enhance adolescents' motivation to quit--by reducing their ambivalence and by providing extrinsic rewards for quitting--and hone their ability to resist pressures to smoke, as opposed to simply obstructing access to cigarettes or making superficial changes to programs designed for adults. In addition, classroom-based cessation programs tended to yield higher quit rates than clinic- or family-based programs or mass-media campaigns. Finally, programs that included more sessions showed higher quit rates.

"I suspect that there are four main elements that are likely to really help," concludes Sussman:

Building teens' intrinsic and extrinsic motivation to quit.

Tailoring programs to adolescents' developmental needs and making the programs fun to attend.

Providing social supports to help teen-agers persevere in their quit attempts.

Showing teens how to make use of community resources that are available.

"The bottom line," says Sussman, "is that no one is going to be able to do it for them, so you have to provide people with the motivation and ability to help themselves."


Tobacco: Health benefits of smoking cessation

Quitting smoking decreases the excess risk of many diseases related to second-hand smoke in children, such as respiratory diseases (e.g., asthma) and ear infections.

Quitting smoking reduces the chances of impotence, having difficulty getting pregnant, having premature births, babies with low birth weights and miscarriage.

1. Mahmud, A, Feely, J. Effect of Smoking on Arterial Stiffness and Pulse Pressure Amplification. Hypertension. 2003 41(1):183-7.

2. U.S. Department of Health and Human Services. The Health Consequences of Smoking: Nicotine Addiction: A Report of the Surgeon General. U.S. Department of Health and Human Services, Public Health Service, Centers for Disease Control, Center for Chronic Disease Prevention and Health Promotion, Office on Smoking and Health. DHHS Publication No. (CDC) 88-8406. 1988.

3. U.S. Department of Health and Human Services. The Health Benefits of Smoking Cessation. U.S. Department of Health and Human Services, Public Health Service, Centers for Disease Control, Center for Chronic Disease Prevention and Health Promotion, Office on Smoking and Health. DHHS Publication No. (CDC) 90-8416. 1990.

4. Doll R, Peto R, Boreham J, Sutherland I. Mortality in relation to smoking: 50 years' observations on male British doctors. BMJ. 2004 328(7455):1519-1527.

5.US Department of Health and Human Services 2004, The Health Consequences of Smoking: A Report of the Surgeon General, US Department of Health and Human Services, Centers for Disease Control and Prevention, National Center for Chronic Disease Prevention and Health Promotion, Office on Smoking and Health, 2004.


Trends in Health Behaviors and Health Outcomes

Along with factors such as genetics and medical care, health behaviors can directly affect health outcomes. Healthy be-haviors such as exercising and eating sensibly lower the risk of conditions like heart disease and diabetes, while unhealthy behaviors such as smoking and excessive drinking raise the risk of conditions like lung cancer and liver disease.

Mortality rates in the U.S. have fallen in recent years - for example, the mortality rate for adults aged 45 to 54 fell by over a quarter between 1979 and 1998. Is healthier behavior responsible for this drop? Or has the drop occurred in spite of an increase in unhealthy behaviors, as a result of other trends like improved medical care? Distinguishing the role of behav-ioral factors from that of medical care is important, since they have different implications for future health care costs and dis-ease burden.

In "Is the U.S. Population Behaving Healthier?" (NBER Working Paper 13013), researchers David Cutler, Edward Glaeser, and Allison Rosen examine trends in health behaviors and estimate their effect on mortality rates.

The data for the analysis come from the National Health and Nutrition Examination Survey, a unique data set that combines data from interviews and physical examinations. In order to examine changes in health behaviors over time, the authors use data for two sample periods, 1971-75 and 1999-2002.

In their analysis, the authors examine three "behavioral risk factors": smoking, obesity, and excessive drinking. Each of these accounts for tens of thousands of deaths in the U.S. each year. They also consider two "biological risk factors" that are the product of other behaviors: high blood pressure and high cholesterol. The authors note that there are other important risk factors such as diabetes status that they are unable to explore due to data limitations.

There have been both positive and negative changes in health behaviors over the past thirty years. On the positive side, smoking and drinking have both declined - the share of the population that currently smokes fell from 40 percent to 25 percent, while the share that drinks heavily fell from 7 percent to 4 percent. Blood pressure and cholesterol have also im-proved markedly - the share of the population with hypertension dropped by two-thirds over this period, while the share with high cholesterol dropped by over one-third. However, there has also been a dramatic increase in obesity, as the share of the population considered overweight or obese has increased from 49 percent to 68 percent.

Given these disparate changes in health behavior, what has been their overall effect on mortality? To answer this question, the authors first use the 1971-75 data to estimate how risk factors relate to whether survey respondents are still alive ten years after the survey. As expected, risk factors have important effects on mortality. For example, being a smoker more than doubles the risk of death in the next ten years. Having hypertension raises the risk by about fifty percent, as does being obese, though the latter effect is smaller and not statistically significant in models that control for blood pressure and cholesterol.

The next step is to use the results of this analysis to estimate mortality risk for each person in the 1971-75 and 1999-2002 surveys. The authors find that mortality risk fell significantly between the two surveys - the average probability of death within ten years for the adult population (aged 25 to 74) fell from 9.8 percent in the earlier survey to 8.4 percent in the later survey, a drop of 1.4 percentage points or 14 percent.

The authors find that the decline in smoking and high blood pressure were the two most important causes of this drop, accounting for 0.9 points and 0.6 points of the drop, respectively. The increase in obesity caused a 0.3 point increase in mortality risk, but this effect was swamped by the positive changes. When the authors convert their results into life expectancies, they find that on net the changes in health behavior over the past thirty years have added 1.8 years to life expectancy at age 25 and 1.4 years to life expectancy at age 65.

Finally, the authors use their estimates to predict what mortality rates might be in the early 2020s if current trends in heath behaviors continue. They note that this is not necessarily a "best guess" of what the future will hold, since trends in health behaviors may change, but nonetheless provides some insight as to where we may be headed.

In their simulations, the share of the population that are current smokers falls from 25 to 15 percent and the share that are overweight and obese rises from 68 percent to 79 percent. Projecting the effect of changes in risk factors on mortality, they find that the drop in smoking would lead to a 0.7 point drop in mortality rates, while the increase in obesity would lead to a surprisingly large 1.1 point increase in mortality rates. The latter result is due to a jump in the share of the population pro-jected to be obese (as opposed to simply overweight), where health risks are particularly severe. The authors also show that when weight gain is accompanied by good control of blood pressure and cholesterol, it has no effect on mortality.

The authors conclude that changes in health behaviors have contributed to a drop in mortality rates over the past thirty years, but caution that future increases in obesity may reverse this trend. Since much of the impact of obesity occurs through hypertension and high cholesterol, better control of these conditions through medication can help blunt the effects of rising obesity. Evaluating the effect of strategies for improving utilization of and adherence to recommended medications, such as pay-for-performance systems to reward physicians or greater use of information technology, is a "high research priority," the authors note.


Tobacco-Related Mortality

Smokeless tobacco is a known cause of cancer. In addition, the nicotine in smokeless tobacco may increase the risk for sudden death from a condition where the heart does not beat properly (ventricular arrhythmias). 5

Tobacco use is the leading preventable cause of death in the United States. 1,3

Cigarettes and Death

Cigarette smoking causes about one of every five deaths in the United States each year. 1,6 Cigarette smoking is estimated to cause the following: 1

  • More than 480,000 deaths annually (including deaths from secondhand smoke)
  • 278,544 deaths annually among men (including deaths from secondhand smoke)
  • 201,773 deaths annually among women (including deaths from secondhand smoke)

Cigarette smoking causes premature death:

  • Life expectancy for smokers is at least 10 years shorter than for nonsmokers. 1,2
  • Quitting smoking before the age of 40 reduces the risk of dying from smoking-related disease by about 90%. 2

Secondhand Smoke and Death

Exposure to secondhand smoke causes an estimated 41,000 deaths each year among adults in the United States: 1

  • Secondhand smoke causes 7,333 annual deaths from lung cancer . 1
  • Secondhand smoke causes 33,951 annual deaths from heart disease . 1

Increased Risk for Death Among Men

  • Men who smoke increase their risk of dying from bronchitis and emphysema by 17 times from cancer of the trachea, lung, and bronchus by more than 23 times. 1
  • Smoking increases the risk of dying from coronary heart disease among middle-aged men by almost four times. 1

Increased Risk for Death Among Women

  • Women who smoke increase their risk of dying from bronchitis and emphysema by 12 times from cancer of the trachea, lung, and bronchus by more than 12 times. 1
  • Between 1960 and 1990, deaths from lung cancer among women increased by more than 500%. 7
  • In 1987, lung cancer surpassed breast cancer to become the leading cause of cancer death among U.S. women. 8
  • In 2000, 67,600 women died from lung cancer. 8
  • During 2010&ndash2014, almost 282,000 women (56,359 women each year) will die from lung cancer. 1
  • Smoking increases the risk of dying from coronary heart disease among middle-aged women by almost five times. 1

Death from Specific Diseases

The following table lists the estimated number of smokers aged 35 years and older who die each year from smoking-related diseases. 1

Annual Cigarette Smoking-Related Mortality in the United States, 2005&ndash2009
Disease Male Female Total
a Other cancers include cancers of the lip, pharynx and oral cavity, esophagus, stomach, pancreas, larynx, cervix uteri (women), kidney and renal pelvis, bladder, liver, colon, and rectum also acute myeloid leukemia
b Other heart diseases includes rheumatic heart disease, pulmonary heart disease, and other forms of heart disease.
c Other vascular diseases include atherosclerosis, aortic aneurysm, and other arterial diseases.
d COPD is chronic obstructive pulmonary disease and includes emphysema, bronchitis, and chronic airways obstruction.
Source: 2014 Surgeon General&rsquos Report: The Health Consequences of Smoking&mdash50 Years of Progress, Chapter 12, Table 12.4 pdf icon [PDF &ndash36 MB] external icon
Cancer
Lung cancer 74,300 53,400 127,700
Other cancers a 26,000 10,000 36,000
Subtotal: Cancer 100,300 63,400 163,700
Cardiovascular Diseases and Metabolic Diseases
Coronary heart disease 61,800 37,500 99,300
Other heart disease b 13,400 12,100 25,500
Cerebrovascular disease 8,200 7,100 15,300
Other vascular disease c 6,000 5,500 11,500
Diabetes mellitus 6,200 2,800 9,000
Subtotal: Cardiovascular and Metabolic 95,600 65,000 160,000
Respiratory Diseases
Pneumonia, influenza, tuberculosis 7,800 4,700 12,500
COPD d 50,400 50,200 100,600
Subtotal: Respiratory 58,200 54,900 113,100
Total: Cancer, Cardiovascular, Metabolic, Respiratory 254,100 183,300 437,400
Perinatal Conditions
Prenatal conditions 346 267 613
Sudden infant death syndrome 236 164 400
Total: Perinatal Conditions 582 431 1,013
Residential Fires 336 284 620
Secondhand Smoke
Lung cancer 4,374 2,959 7,333
Coronary heart disease 19,152 14,799 33,951
Total: Secondhand smoke 23,526 17,758 41,284
TOTAL Attributable Deaths 278,544 201,773 480,317

References

  1. U.S. Department of Health and Human Services. The Health Consequences of Smoking&mdash50 Years of Progress. A Report of the Surgeon General . Atlanta: U.S. Department of Health and Human Services, Centers for Disease Control and Prevention, National Center for Chronic Disease Prevention and Health Promotion, Office on Smoking and Health, 2014 [accessed 2015 Aug 17].
  2. Jha P, Ramasundarahettige C, Landsman V, Rostrom B, Thun M, Anderson RN, McAfee T, Peto R . 21st Century Hazards of Smoking and Benefits of Cessation in the United States [PDF &ndash738 KB] external icon . New England Journal of Medicine, 2013368(4):341&ndash50 [accessed 2015 Aug 17].
  3. U.S. Department of Health and Human Services. The Health Consequences of Smoking: A Report of the Surgeon General . Atlanta: U.S. Department of Health and Human Services, Centers for Disease Control and Prevention, National Center for Chronic Disease Prevention and Health Promotion, Office on Smoking and Health, 2004 [accessed 2015 Aug 17].
  4. National Cancer Institute. Cigars: Health Effects and Trends external icon . Smoking and Tobacco Control Monograph No. 9. Bethesda (MD): U.S. Department of Health and Human Services, National Institutes of Health, National Cancer Institute, 1998. [accessed 2015 Aug 17].
  5. World Health Organization. Smokeless Tobacco and Some Tobacco-Specific N-Nitrosamines pdf icon [PDF &ndash3.18 MB] external icon . International Agency for Research on Cancer Monographs on the Evaluation of Carcinogenic Risks to Humans Vol. 89. Lyon, (France): World Health Organization, International Agency for Research on Cancer, 2007 [accessed 2015 Aug 17].
  6. Centers for Disease Control and Prevention. QuickStats: Number of Deaths from 10 Leading Causes&mdashNational Vital Statistics System, United States, 2010 . Morbidity and Mortality Weekly Report 2013: 62(08)155 [accessed 2015 Aug 17].
  7. Novotny TE, Giovino GA. Tobacco Use . In: Brownson RC, Remington PL, Davis JR, editors. Chronic Disease Epidemiology and Control. Washington: American Public Health Association, 1998:117&ndash48 [cited 2015 Aug 17].
  8. U.S. Department of Health and Human Services. Women and Smoking: A Report of the Surgeon General . Atlanta: U.S. Department of Health and Human Services, Centers for Disease Control and Prevention, Coordinating Center for Health Promotion, National Center for Chronic Disease Prevention and Health Promotion, Office on Smoking and Health, 2001 [accessed 2015 Aug 17].

For Further Information

Centers for Disease Control and Prevention
National Center for Chronic Disease Prevention and Health Promotion
Office on Smoking and Health
E-mail: [email protected]
Phone: 1-800-CDC-INFO

Media Inquiries: Contact CDC&rsquos Office on Smoking and Health press line at 770-488-5493.


Methods

Ethics statement

Ethical approval was obtained from the ethics committees of each of the 18 CSHA study centres. Written, informed consent for participation in the CSHA was obtained from each participant or proxy respondent.

The sample came from the second phase of the CSHA-2 (conducted in 1996–97). The CSHA is a representative study of dementia and other health problems in older Canadians aged 65 and older [ 19]. At baseline (CSHA-1, 1990–91), participants (n = 10,263, of whom 9,008 were community dwelling) were sampled in a population-representative manner from English- and French-speaking older Canadians, though those living in the Yukon or Northwest Territories, residents of Aboriginal Reserves or military bases, and those with an immediately life-threatening illness were excluded. The sample was clustered within five Canadian regions and stratified by age, with over-sampling of those aged 75 and older. The baseline data collection occurred in 1991, with follow-up by interview and/or clinical examination or vital status verification at 5 (CSHA-2) and 10 (CSHA-3) years [ 19]. Of 5,703 participants in the CSHA-2 screening interview, 729 (13%) were missing frailty status. People with missing frailty data were older (83.2 versus 78.5 years, P < 0.0001), often relying on proxy respondents. The fittest individuals were defined as the 584 who were in the ‘zero state’ of frailty, i.e. those in whom either 0 or only 1 health deficit were reported. Of these, 541 (93%) had social vulnerability status data allowing for a social vulnerability index to be calculated. The cohort was followed for 5 years, at which time vital status was known for all but 3 of these 541 individuals 95 (17.6%) had died.

Frailty and social vulnerability were based on self-report. The social vulnerability index has been described in detail elsewhere [ 17]. It includes 40 items addressing various social domains including living situation, marital status, social engagement, social support, feelings of mastery and empowerment and socio-economic status. Our previous investigations of its properties have supported its use as a holistic measure in particular, no single item or group of items has been found to drive associations with health outcomes [ 17, 18]. Responses to 40 social variables were assigned a value of ‘1’ if representing a deficit and ‘0’ otherwise. The sum of this deficit count, divided by 40, is the social vulnerability index, so that the theoretical range is from 0 (none of the 40 social deficits) to 1 (all 40/40 social deficits) higher scores indicate greater vulnerability [ 17]. Social vulnerability was divided into tertiles of low, intermediate and high index values. Individuals missing data for one or two of the social deficits were assigned to the tertile of social vulnerability based on their existing data. The 43 individuals who were missing more than one to two variables were coded as missing social vulnerability status and therefore excluded from further analysis.

The frailty index was operationalised using 31 health deficits (illnesses, symptoms and functional problems). Both the frailty and social vulnerability indices have been validated and lists of their constituent variables have been published [ 6, 17]. The association between level of social vulnerability (independent variable) and mortality (dependent variable) was analysed using Cox regression. The absolute risk of mortality was calculated for the three strata of social vulnerability, and Kaplan–Meier survival curves were generated.

Exercise, smoking and alcohol histories were obtained from a self-reported questionnaire conducted at the first phase of CSHA, 5 years prior to the baseline data for this analysis. These variables were not updated in later phases of the CSHA, so previous reported history is all that was available. Exercise was categorised as high (regular exercise, three or more times per week, more intense than walking), intermediate (regular exercise but either less frequent or of walking intensity or less) and low (no regular exercise reported). This definition of exercise has been validated in the CSHA [ 20, 21]. Smoking was defined as a lifetime history of ever having ‘smoked cigarettes, pipe or cigars regularly (nearly every day)’. Alcohol intake was similarly defined as ever having been a regular drinker of beer, wine or spirits.


Discussion

Cumulative empirical evidence across 148 independent studies indicates that individuals' experiences within social relationships significantly predict mortality. The overall effect size corresponds with a 50% increase in odds of survival as a function of social relationships. Multidimensional assessments of social integration yielded an even stronger association: a 91% increase in odds of survival. Thus, the magnitude of these findings may be considered quite large, rivaling that of well-established risk factors (Figure 6). Results also remained consistent across a number of factors, including age, sex, initial health status, follow-up period, and cause of death, suggesting that the association between social relationships and mortality may be generalized.

Note: Effect size of zero indicates no effect. The effect sizes were estimated from meta analyses: A = Shavelle, Paculdo, Strauss, and Kush, 2008 [205] B = Critchley and Capewell, 2003 [206] C = Holman, English, Milne, and Winter, 1996 [207] D = Fine, Smith, Carson, Meffe, Sankey, Weissfeld, Detsky, and Kapoor, 1994 [208] E = Taylor, Brown, Ebrahim, Jollife, Noorani, Rees et al., 2004 [209] F, G = Katzmarzyk, Janssen, and Ardern, 2003 [210] H = Insua, Sacks, Lau, Lau, Reitman, Pagano, and Chalmers, 1994 [211] I = Schwartz, 1994 [212].

The magnitude of risk reduction varied depending on the type of measurement of social relationships (see Table 4). Social relationships were most highly predictive of reduced risk of mortality in studies that included multidimensional assessments of social integration. Because these studies included more than one type of social relationship measurement (e.g., network based inventories, marital status, etc.), such a measurement approach may better represent the multiple pathways (described earlier) by which social relationships influence health and mortality [182]. Conversely, binary evaluations of living alone (yes/no) were the least predictive of mortality status. The reliability and validity of measurement likely explains this finding, and researchers are encouraged to use psychometrically sound measures of social relationships (e.g., Table 2). For instance, while researchers may be tempted to use a simple single-item such as “living alone” as a proxy for social isolation, it is possible for one to live alone but have a large supportive social network and thus not adequately capture social isolation. We also found that social isolation had a similar influence on likelihood of mortality compared with other measures of social relationships. This evidence qualifies the notion of a threshold effect (lack of social relationships is the only detrimental condition) rather, the association appears robust across a variety of types of measures of social relationships.

This meta-analysis also provides evidence to support the directional influence of social relationships on mortality. Most of the studies (60%) involved community cohorts, most of whom would not be experiencing life-threatening conditions at the point of initial evaluation. Moreover, initial health status did not moderate the effect of social relationships on mortality. Although illness may result in poorer or more restricted social relationships (social isolation resulting from physical confinement), such that individuals closer to death may have decreased social support compared to healthy individuals, the findings from these studies indicate that general community samples with strong social relationships are likely to remain alive longer than similar individuals with poor social relations. However, causality is not easily established. One cannot randomly assign human participants to be socially isolated, married, or in a poor-quality relationship. A similar dilemma characterizes virtually all lifestyle risk factors for mortality: for instance, one cannot randomly assign individuals to be smokers or nonsmokers. Despite such challenges, “smoking represents the most extensively documented cause of disease ever investigated in the history of biomedical research” [183]. The link between social relationships and mortality is currently much less understood than other risk factors nonetheless there is substantial experimental, cross-sectional, and prospective evidence linking social relationships with multiple pathways associated with mortality (see [182] for review). Existing models for reducing risk of mortality may be substantially strengthened by including social relationship factors.

Notably, the overall effect for social relationships on mortality reported here may be a conservative estimate. Many studies included in the meta-analysis utilized single item measures of social relations, yet the magnitude of the association was greatest among those studies utilizing complex assessments. Moreover, because many studies statistically adjusted for standard risk factors, the effect may be underestimated, since some of the impact of social relationships on mortality may be mediated through such factors (e.g., behavior, diet, exercise). Additionally, most measures of social relations did not take into account the quality of the social relationships, thereby assuming that all relationships are positive. However, research suggests this is not the case, with negative social relationships linked to greater risk of mortality [184],[185]. For instance, marital status is widely used as a measure of social integration however, a growing literature documents its divergent effects based on level of marital quality [186],[187]. Thus the effect of positive social relationships on risk of mortality may actually be much larger than reported in this meta-analysis, given the failure to account for negative or detrimental social relationships within the measures utilized across studies.

Other possible limitations of this review should be acknowledged. Statistical controls (e.g., age, sex, physical condition, etc.) employed by many of the studies rule out a number of potentially confounding variables that might account for the association between social relationships and mortality. However, studies used an inconsistent variety of controlling variables, and some reports involved raw data (Table 1). Although effect size magnitude was diminished by the inclusion of statistical controls only within the data obtained by measures of structural social relationships (but not functional or combined measures), future research can better specify which variables are most likely to impact the overall association. It must also be acknowledged that existing data primarily represent research conducted in North America and Western Europe. Although we found no differences across world region, future reviews inclusive of research written in all languages (not only English) with participants better representing other world regions may yield better estimates across populations.

Approximately two decades after the review by House and colleagues [1], a generation of empirical research validates their initial premise: Social relationships exert an independent influence on risk for mortality comparable with well established risk factors for mortality (Figure 6). Although limited by the state of current investigations and possible omission of pertinent reports, this meta-analysis provides empirical evidence (nearly 30 times the number of studies previously reported) to support the criteria for considering insufficient social relationships a risk factor of mortality (i.e., strength and consistency of association across a wide range of studies, temporal ordering, and gradient of response) [188]. The magnitude of the association between social relationships and mortality has now been established, and this meta-analysis provides much-needed clarification regarding the social relationship factor(s) most predictive of mortality. Future research can shift to more nuanced questions aimed at (a) understanding the causal pathways by which social participation promotes health, (b) refining conceptual models, and (c) developing effective intervention and prevention models that explicitly account for social relations.

Some steps have already been taken identifying the psychological, behavioral, and physiological pathways linking social relationships to health [5],[182],[189]. Social relationships are linked to better health practices and to psychological processes, such as stress and depression, that influence health outcomes in their own right [190] however, the influence of social relationships on health cannot be completely explained by these processes, as social relationships exert an independent effect. Reviews of such findings suggest that there are multiple biologic pathways involved (physiologic regulatory mechanisms, themselves intertwined) that in turn influence a number of disease endpoints [182],[191]–[193]. For instance, a number of studies indicate that social support is linked to better immune functioning [194]–[197] and to immune-mediated inflammatory processes [198]. Thus interdisciplinary work and perspective will be important in future studies given the complexity of the phenomenon.

Perhaps the most important challenge posed by these findings is how to effectively utilize social relationships to reduce mortality risk. Preliminary investigations have demonstrated some risk reduction through formalized social interventions [199]. While the evidence is mixed [2],[6], it should be noted that most social support interventions evaluated in the literature thus far are based on support provided from strangers in contrast, evidence provided in this meta-analysis is based almost entirely on naturally occurring social relationships. Moreover, our analyses suggest that received support is less predictive of mortality than social integration (Table 4). Therefore, facilitating patient use of naturally occurring social relations and community-based interventions may be more successful than providing social support through hired personnel, except in cases in which patient social relations appear to be detrimental or absent. Multifaceted community-based interventions may have a number of advantages because such interventions are socially grounded and include a broad cross-section of the public. Public policy initiatives need not be limited to those deemed “high risk” or those who have already developed a health condition but could potentially include low- and moderate-risk individuals earlier in the risk trajectory [200]. Overall, given the significant increase in rate of survival (not to mention quality of life factors), the results of this meta-analysis are sufficiently compelling to promote further research aimed at designing and evaluating interventions that explicitly account for social relationship factors across levels of health care (prevention, evaluation, treatment compliance, rehabilitation, etc.).

Conclusion

Data across 308,849 individuals, followed for an average of 7.5 years, indicate that individuals with adequate social relationships have a 50% greater likelihood of survival compared to those with poor or insufficient social relationships. The magnitude of this effect is comparable with quitting smoking and it exceeds many well-known risk factors for mortality (e.g., obesity, physical inactivity). These findings also reveal significant variability in the predictive utility of social relationship variables, with multidimensional assessments of social integration being optimal when assessing an individual's risk for mortality and evidence that social isolation has a similar influence on mortality to other measures of social relationships. The overall effect remained consistent across a number of factors, including age, sex, initial health status, follow-up period, and cause of death, suggesting that the association between social relationships and mortality may be general, and efforts to reduce risk should not be isolated to subgroups such as the elderly.

To draw a parallel, many decades ago high mortality rates were observed among infants in custodial care (i.e., orphanages), even when controlling for pre-existing health conditions and medical treatment [201]–[204]. Lack of human contact predicted mortality. The medical profession was stunned to learn that infants would die without social interaction. This single finding, so simplistic in hindsight, was responsible for changes in practice and policy that markedly decreased mortality rates in custodial care settings. Contemporary medicine could similarly benefit from acknowledging the data: Social relationships influence the health outcomes of adults.

Physicians, health professionals, educators, and the public media take risk factors such as smoking, diet, and exercise seriously the data presented here make a compelling case for social relationship factors to be added to that list. With such recognition, medical evaluations and screenings could routinely include variables of social well-being medical care could recommend if not outright promote enhanced social connections hospitals and clinics could involve patient support networks in implementing and monitoring treatment regimens and compliance, etc. Health care policies and public health initiatives could likewise benefit from explicitly accounting for social factors in efforts aimed at reducing mortality risk. Individuals do not exist in isolation social factors influence individuals' health though cognitive, affective, and behavioral pathways. Efforts to reduce mortality via social relationship factors will require innovation, yet innovation already characterizes many medical interventions that extend life at the expense of quality of life. Social relationship–based interventions represent a major opportunity to enhance not only the quality of life but also survival.


Lesson 3: Measures of Risk

A measure of public health impact is used to place the association between an exposure and an outcome into a meaningful public health context. Whereas a measure of association quantifies the relationship between exposure and disease, and thus begins to provide insight into causal relationships, measures of public health impact reflect the burden that an exposure contributes to the frequency of disease in the population. Two measures of public health impact often used are the attributable proportion and efficacy or effectiveness.

Attributable proportion

Definition of attributable proportion

The attributable proportion, also known as the attributable risk percent, is a measure of the public health impact of a causative factor. The calculation of this measure assumes that the occurrence of disease in the unexposed group represents the baseline or expected risk for that disease. It further assumes that if the risk of disease in the exposed group is higher than the risk in the unexposed group, the difference can be attributed to the exposure. Thus, the attributable proportion is the amount of disease in the exposed group attributable to the exposure. It represents the expected reduction in disease if the exposure could be removed (or never existed).

Appropriate use of attributable proportion depends on a single risk factor being responsible for a condition. When multiple risk factors may interact (e.g., physical activity and age or health status), this measure may not be appropriate.

Method for calculating attributable proportion

Attributable proportion is calculated as follows:

Risk for exposed group &minus risk for unexposed group Risk for exposed group

Attributable proportion can be calculated for rates in the same way.

EXAMPLE: Calculating Attributable Proportion

In another study of smoking and lung cancer, the lung cancer mortality rate among nonsmokers was 0.07 per 1,000 persons per year.(14) The lung cancer mortality rate among persons who smoked 1&ndash14 cigarettes per day was 0.57 lung cancer deaths per 1,000 persons per year. Calculate the attributable proportion.

Attributable proportion = (0.57 &minus 0.07) &frasl 0.57 × 100% = 87.7%

Given the proven causal relationship between cigarette smoking and lung cancer, and assuming that the groups are comparable in all other ways, one could say that about 88% of the lung cancer among smokers of 1 14 cigarettes per day might be attributable to their smoking. The remaining 12% of the lung cancer cases in this group would have occurred anyway.

Vaccine efficacy or vaccine effectiveness

Vaccine efficacy and vaccine effectiveness measure the proportionate reduction in cases among vaccinated persons. Vaccine efficacy is used when a study is carried out under ideal conditions, for example, during a clinical trial. Vaccine effectiveness is used when a study is carried out under typical field (that is, less than perfectly controlled) conditions.

Vaccine efficacy/effectiveness (VE) is measured by calculating the risk of disease among vaccinated and unvaccinated persons and determining the percentage reduction in risk of disease among vaccinated persons relative to unvaccinated persons. The greater the percentage reduction of illness in the vaccinated group, the greater the vaccine efficacy/effectiveness. The basic formula is written as:

Risk among unvaccinated group &minus risk among vaccinated group Risk among unvaccinated group

In the first formula, the numerator (risk among unvaccinated &minus risk among vaccinated) is sometimes called the risk difference or excess risk.

Vaccine efficacy/effectiveness is interpreted as the proportionate reduction in disease among the vaccinated group. So a VE of 90% indicates a 90% reduction in disease occurrence among the vaccinated group, or a 90% reduction from the number of cases you would expect if they have not been vaccinated.

EXAMPLE: Calculating Vaccine Effectiveness

Calculate the vaccine effectiveness from the varicella data in Table 3.13.

VE = (42.9 &minus 11.8) &frasl 42.9 = 31.1 &frasl 42.9 = 72%

Alternatively, VE = 1 &minus RR = 1 &minus 0.28 = 72%

So, the vaccinated group experienced 72% fewer varicella cases than they would have if they had not been vaccinated.



Comments:

  1. Kektilar

    I can not decide.

  2. Jem

    old fashioned

  3. JoJojora

    Not caught, not high! Why is it called prayer when you talk to God, and schizophrenia when God is with you? When you decide to shake off the old days, make sure that it does not fall off !!! Anything good in life is either illegal, immoral, or obese



Write a message