Related Articles

Background

Comorbidity complicates estimations of health-adjusted life expectancy (HALE) using disease prevalences and disability weights from Burden of Disease studies. Usually, the exact amount of comorbidity is unknown and no disability weights are defined for comorbidity.

Methods

Using data of the Dutch national burden of disease study, the effects of different methods to adjust for comorbidity on HALE calculations are estimated. The default multiplicative adjustment method to define disability weights for comorbidity is compared to HALE estimates without adjustment for comorbidity and to HALE estimates in which the amount of disability in patients with multiple diseases is solely determined by the disease that leads to most disability (the maximum adjustment method). To estimate the amount of comorbidity, independence between diseases is assumed.

Results

Compared to the multiplicative adjustment method, the maximum adjustment method lowers HALE estimates by 1.2 years for males and 1.9 years for females. Compared to no adjustment, a multiplicative adjustment lowers HALE estimates by 1.0 years for males and 1.4 years for females.

Conclusion

The differences in HALE caused by the different adjustment methods demonstrate that adjusting for comorbidity in HALE calculations is an important topic that needs more attention. More empirical research is needed to develop a more general theory as to how comorbidity influences disability.

Background

Elevated levels of body mass index (BMI) and smoking are well established lifestyle risk factors for coronary heart disease (CHD) and stroke. If these two risk factors have a synergistic relationship, rigorous lifestyle modification may contribute to greater reduction in cardiovascular burden than previously expected.

Methods

A pooled analysis of individual participant data from 38 cohorts, involving 378,579 participants. Hazards ratios (HRs) and 95% confidence intervals (CIs) for BMI by cigarette smoking status were estimated using Cox proportional hazard models.

Results

During a mean follow-up of 3.8 years, 2706 CHD and 3264 strokes were recorded. There was a log-linear, positive relationship of BMI with CHD and stroke in both smokers and non-smokers with evidence of a synergistic effect of smoking on the association between BMI and CHD only: HRs (95% CIs) associated with a 2 kg/m2 higher BMI were 1.13 (1.10 – 1.17) in current smokers and 1.09 (1.06 – 1.11) in non-smokers (p-value for interaction = 0.04).

Conclusion

Smoking amplifies the positive association between BMI and CHD but not stroke. If confirmed, these results suggest that effective strategies that target smoking cessation and weight loss are likely to have a greater impact than anticipated on reducing the burden of CHD.

Purpose

Localized prostate cancer (LPC) patients are faced with numerous treatment options, including observation or watchful waiting. The choice of treatment largely depends on their baseline health-adjusted life expectancy (HALE). By consensus, physicians recommend treatment if the patient’s HALE is ten or more years. However, the estimation of HALE is difficult. Although subjective by nature, self-rated health (SRH) is a robust predictor of mortality. We studied the usefulness of SRH in estimating HALE in patients who are considering treatment for LPC.

Methods

A total of 144 LPC patients from a large urology private practice in Norfolk, Virginia, were surveyed before they had chosen a treatment option.

Results

HALE determined by SRH correlated well with objective health measures, and was higher than age-based life expectancy by an average of 2 years. The observed difference in life expectancy due to SRH adjustment was higher among patients with a better socioeconomic and health profile.

Conclusions

SRH is an easy-to-use indicator of HALE in LPC patients. A table for HALE estimation by age and SRH is provided for men aged 70-80 years. Additional research with larger samples and prospective study designs are needed before the SRH method can be used in primary care and urology settings.

Objective

To estimate the lifetime financial burden on Taiwan's national health insurance (NHI) system, life expectancy and years of life expectancy lost (YLEL) attributable to smoking from major smoking related diseases.

Methods

10 major smoking related diseases (seven cancers, stroke, acute myocardial infarction and chronic obstructive pulmonary disease) were selected for this study. A survival analysis was conducted on linked cohorts from the National Death Registry database and the National Cancer Registry (NCR) and patients at the National Taiwan University Hospital (NTUH). Estimation of the smoking attributable fraction (SAF) for the study diseases was undertaken by combining the relative risks of smokers against non‐smokers and the prevalence of smoking in Taiwan. The YLEL attributable to smoking was calculated for the study diseases by combining the survival analysis results, the SAF and the annual incidences of each disease. The lifetime medical expenditure for the study diseases was estimated by integrating the survival curve and the mean annual medical costs calculated from NHI reimbursement records.

Results

There were 241 280 incidents of the 10 study diseases in 2001, of which about 53 648 cases (22.2%) were attributable to smoking, with a total YLEL of 191 313 at an average of about 3.6 YLEL per case. For each case, the average survival time was about 10.2 years. Under two different annual discount rates, the total lifetime financial burden on the NHI was estimated at between $291 million (£147 million; €216 million) (3% discount) and $336 million (1% discount) for all diseases attributable to smoking in 2001, accounting for about 24.6% of the total estimated lifetime medical expenditure for all incidents of the 10 study diseases.

Conclusions

Smoking places tremendous financial and health burdens upon both society and individuals. A much more stringent tobacco control strategy is needed to curb the damage from smoking.

Background

Obesity is a major cause of morbidity and mortality and is associated with high medical expenditures. It has been suggested that obesity prevention could result in cost savings. The objective of this study was to estimate the annual and lifetime medical costs attributable to obesity, to compare those to similar costs attributable to smoking, and to discuss the implications for prevention.

Methods and Findings

With a simulation model, lifetime health-care costs were estimated for a cohort of obese people aged 20 y at baseline. To assess the impact of obesity, comparisons were made with similar cohorts of smokers and “healthy-living” persons (defined as nonsmokers with a body mass index between 18.5 and 25). Except for relative risk values, all input parameters of the simulation model were based on data from The Netherlands. In sensitivity analyses the effects of epidemiologic parameters and cost definitions were assessed. Until age 56 y, annual health expenditure was highest for obese people. At older ages, smokers incurred higher costs. Because of differences in life expectancy, however, lifetime health expenditure was highest among healthy-living people and lowest for smokers. Obese individuals held an intermediate position. Alternative values of epidemiologic parameters and cost definitions did not alter these conclusions.

Conclusions

Although effective obesity prevention leads to a decrease in costs of obesity-related diseases, this decrease is offset by cost increases due to diseases unrelated to obesity in life-years gained. Obesity prevention may be an important and cost-effective way of improving public health, but it is not a cure for increasing health expenditures.

Using a simulation model, Pieter van Baal and colleagues conclude that obesity prevention leads to a decrease in costs of obesity-related diseases, but this is offset by cost increases due to diseases unrelated to obesity in life-years gained.

Editors' Summary

Background.

Since the mid 1970s, the proportion of people who are obese (people who have an unhealthy amount of body fat) has increased sharply in many countries. One-third of all US adults, for example, are now classified as obese, and recent forecasts suggest that by 2025 half of US adults will be obese. A person is overweight if their body mass index (BMI, calculated by dividing their weight in kilograms by their height in meters squared) is between 25 and 30, and obese if BMI is greater than 30. Compared to people with a healthy weight (a BMI between 18.5 and 25), overweight and obese individuals have an increased risk of developing many diseases, such as diabetes, coronary heart disease and stroke, and tend to die younger. People become unhealthily fat by consuming food and drink that contains more energy than they need for their daily activities. In these circumstances, the body converts the excess energy into fat for use at a later date. Obesity can be prevented, therefore, by having a healthy diet and exercising regularly.

Why Was This Study Done?

Because obesity causes so much illness and premature death, many governments have public-health policies that aim to prevent obesity. Clearly, the improvement in health associated with the prevention of obesity is a worthwhile goal in itself but the prevention of obesity might also reduce national spending on medical care. It would do this, the argument goes, by reducing the amount of money spent on treating the diseases for which obesity is a risk factor. However, some experts have suggested that these short-term savings might be offset by spending on treating the diseases that would occur during the extra lifespan experienced by non-obese individuals. In this study, therefore, the researchers have used a computer model to calculate yearly and lifetime medical costs associated with obesity in The Netherlands.

What Did the Researchers Do and Find?

The researchers used their model to estimate the number of surviving individuals and the occurrence of various diseases for three hypothetical groups of men and women, examining data from the age of 20 until the time when the model predicted that everyone had died. The “obese” group consisted of never-smoking people with a BMI of more than 30; the “healthy-living” group consisted of never-smoking people with a healthy weight; the “smoking” group consisted of lifetime smokers with a healthy weight. Data from the Netherlands on the costs of illness were fed into the model to calculate the yearly and lifetime health-care costs of all three groups. The model predicted that until the age of 56, yearly health costs were highest for obese people and lowest for healthy-living people. At older ages, the highest yearly costs were incurred by the smoking group. However, because of differences in life expectancy (life expectancy at age 20 was 5 years less for the obese group, and 8 years less for the smoking group, compared to the healthy-living group), total lifetime health spending was greatest for the healthy-living people, lowest for the smokers, and intermediate for the obese people.

What Do These Findings Mean?

As with all mathematical models such as this, the accuracy of these findings depend on how well the model reflects real life and the data fed into it. In this case, the model does not take into account varying degrees of obesity, which are likely to affect lifetime health-care costs, nor indirect costs of obesity such as reduced productivity. Nevertheless, these findings suggest that although effective obesity prevention reduces the costs of obesity-related diseases, this reduction is offset by the increased costs of diseases unrelated to obesity that occur during the extra years of life gained by slimming down.

Additional Information.

Please access these Web sites via the online version of this summary at http://dx.doi.org/doi:10.1371/journal.pmed.0050029.

The MedlinePlus encyclopedia has a page on obesity (in English and Spanish)

The US Centers for Disease Control and Prevention provides information on all aspects of obesity (in English and Spanish)

The UK National Health Service's health Web site (NHS Direct) provides information about obesity

The International Obesity Taskforce provides information about preventing obesity

The UK Foods Standards Agency, the United States Department of Agriculture, and Shaping America's Health all provide useful advice about healthy eating

The Netherlands National Institute for Public Health and the Environment (RIVM) Web site provides more information on the cost of illness and illness prevention in the Netherlands (in English and Dutch)

Background

Healthy life expectancy – sometimes called health-adjusted life expectancy (HALE) – is a form of health expectancy indicator that extends measures of life expectancy to account for the distribution of health states in the population. The World Health Organization has estimated healthy life expectancy for 192 WHO Member States using information from health interview surveys and from the Global Burden of Disease Study. The latter estimates loss of health by cause, age and sex for populations. Summation of prevalent years lived with disability (PYLD) across all causes would result in overestimation of the severity of the population average health state because of comorbidity between conditions. Earlier HALE calculations made adjustments for independent comorbidity in adding PYLD across causes. This paper presents a method for adjusting for dependent comorbidity using available empirical data.

Methods

Data from five large national health surveys were analysed by age and sex to estimate "dependent comorbidity" factors for pairs of conditions. These factors were defined as the ratio of the prevalence of people with both conditions to the product of the two total prevalences for each of the conditions. The resulting dependent comorbidity factors were used for all Member States to adjust for dependent comorbidity in summation of PYLD across all causes and in the calculation of HALE. A sensitivity analysis was also carried out for order effects in the proposed calculation method.

Results

There was surprising consistency in the dependent comorbidity factors across the five surveys. The improved estimation of dependent comorbidity resulted in reductions in total PYLD per capita ranging from a few per cent in younger adult ages to around 8% in the oldest age group (80 years and over) in developed countries and up to 15% in the oldest age group in the least developed countries. The effect of the dependent comorbidity adjustment on estimated healthy life expectancies is small for some regions (high income countries, Eastern Europe, Western Pacific) and ranges from an increase of 0.5 to 1.5 years for countries in Latin America, South East Asia and Sub-Saharan Africa.

Conclusion

The available evidence suggests that dependent comorbidity is important, and that adjustment for it makes a significant difference to resulting HALE estimates for some regions of the world. Given the data limitations, we recommend a normative adjustment based on the available evidence, and applied consistently across all countries.

Cardiovascular disease (CVD) is the driving force behind the discrepancy in life expectancy between indigenous and non-indigenous groups in many countries. Preceding CVD many indigenous groups exhibit a cluster of cardiometabolic risk factors, including overweight-obesity, diabetes, high cholesterol, and high blood pressure. In turn, modifiable lifestyle risk factors contribute to the development of this cluster of cardiometabolic conditions. Modifiable lifestyle risk factors include, but are not limited to, physical inactivity, poor nutrition, excessive alcohol consumption, and cigarette smoking. Notably, these metabolic and lifestyle risk factors are relatively simple to monitor and track. The current review will look at modifiable cardiometabolic (overweight-obesity, diabetes mellitus, high cholesterol, and high blood pressure) and lifestyle (physical inactivity, poor nutrition, risky alcohol behavior, and cigarette smoking) risk factors among indigenous populations from Australia (Aboriginal Australians and Torres Strait Islanders), New Zealand (Māori) and the United States (Native Americans). Discussion will focus on the causal relationship between modifiable lifestyle risk factors and cardiometabolic outcomes, as well as, simple measurements for tracking these risk factors.

Obesity is a risk factor for asthma, particularly in women, but few cohort studies have evaluated abdominal obesity, which reflects metabolic differences in visceral fat known to influence systemic inflammation. We examined the relationships of asthma prevalence with measures of abdominal obesity and adult weight gain, in addition to body mass index (BMI), in a large cohort of female teachers. We calculated prevalence odds ratios (ORs) for current asthma using multivariable linear modeling, adjusting for age, smoking, and race/ethnicity. Of the 88,304 women in the analyses, 13% (11,500) were obese (BMI ≥ 30 kg/m2) at baseline; 1,334 were extremely obese (BMI ≥ 40). Compared to those of normal weight, the adjusted OR for adult-onset asthma increased from 1.40 (95% confidence interval (CI): 1.31, 1.49) for overweight women to 3.30 (95% CI: 2.85, 3.82) for extremely obese women. Large waist circumference (> 88 cm) was associated with increased asthma prevalence even among women with a normal BMI (OR = 1.37, 95% CI: 1.18, 1.59). Among obese women, the OR for asthma was greater among those who were also abdominally obese compared to women whose waist was ≤ 88 cm (2.36 vs. 1.57). Obese and overweight women were at greater risk of severe asthma episodes, measured by urgent medical visits and hospitalizations. This study confirms the association between excess weight and asthma severity and prevalence, and showed that a large waist was associated with increased asthma prevalence even among women considered to have normal body weight.

Background

Obese individuals who smoke have a 14 year reduction in life expectancy. Both obesity and smoking are independantly associated with increased risk of malignancy. Natural killer cells (NK) are critical mediators of anti-tumour immunity and are compromised in obese patients and smokers. We examined whether NK cell function was differentially affected by cigarette smoke in obese and lean subjects.

Methodology and Principal Findings

Clinical data and blood were collected from 40 severely obese subjects (BMI>40 kg/m2) and 20 lean healthy subjects. NK cell levels and function were assessed using flow cytometry and cytotoxicity assays. The effect of cigarette smoke on NK cell ability to kill K562 tumour cells was assessed in the presence or absence of the adipokines leptin and adiponectin. NK cell levels were significantly decreased in obese subjects compared to lean controls (7.6 vs 16.6%, p = 0.0008). NK function was also significantly compromised in obese patients (30% +/− 13% vs 42% +/−12%, p = 0.04). Cigarette smoke inhibited NK cell ability to kill tumour cell lines (p<0.0001). NK cells from obese subjects were even more susceptible to the inhibitory effects of smoke compared to lean subjects (33% vs 28%, p = 0.01). Cigarette smoke prevented NK cell activation, as well as perforin and interferon-gamma secretion upon tumour challenge. Adiponectin but not leptin partially reversed the effects of smoke on NK cell function in both obese (p = 0.002) and lean controls (p = 0.01).

Conclusions/Significance

Obese subjects have impaired NK cell activity that is more susceptible to the detrimental effects of cigarette smoke compared to lean subjects. This may play a role in the increase of cancer and infection seen in this population. Adiponectin is capable of restoring NK cell activity and may have therapeutic potential for immunity in obese subjects and smokers.

Objectives

To estimate the health-adjusted life expectancy (HALE) from diabetes mellitus (DM) using a population health survey linked to a population-based DM registry.

Methods

The 1996/97 Ontario Health Survey (N = 35,517) was linked to the Ontario Diabetes Database (N = 487,576). The Health Utilities Index (HUI3) was used to estimate health-related quality of life. HALE was estimated using an adapted Sullivan method.

Results

Life expectancy at birth of people with DM was 64.7 and 70.7 years for men and women – 12.8 and 12.2 years less than for men and women without DM. The HUI3 was lower for physician-diagnosed DM compared to self-reported DM (0.799 versus 0.872). HALE at birth was 58.3 and 62.8 years for men and women – 11.9 and 10.7 years less than that of men and women without DM.

Conclusions

The linked data approach demonstrates that DM is an important cause of disease burden. This approach reduces assumptions when estimating the prevalence and severity of disability from DM compared to methods that rely on self-reported disease status or indirect assessment of disability severity.

Background

Nationwide surveys in the United States found that certain health-related factors, in particular cigarette smoking and obesity, were more prevalent in veterans than in non-veterans.

Purpose

The objective of this paper was to compare health-related characteristics and dietary intakes between veterans and non-veterans in the Multiethnic Cohort.

Materials and Methods

The cohort participants (aged 45–75 years), residing in Hawaii and California at baseline, completed a mailed questionnaire on diet, medical history, and lifestyle in 1993–1996. The current analyses included 20,939 men (14,975 veterans and 5,964 non-veterans) who returned a survey questionnaire on military service in 2007.

Results

Compared to non-veterans, veterans were more likely to be overweight and obese (BMI≥25, 61% vs. 55%), former smokers (54% vs. 47%), heavier consumers of red and processed meat, and lighter consumers of fruits and vegetables. Within the veteran group, enlisted men were more likely to be obese, to have a history of smoking, to consume more processed meat and to consume smaller amounts of dairy products and fruits than officers.

Conclusion

The findings imply that veterans as a group are at somewhat higher risk of developing lifestyle-related chronic diseases than are non-veterans. Comparisons of actual differences in disease incidence and mortality in the Multiethnic Cohort between veterans and non-veterans will require several more years of follow-up.

We applied our previously developed estimation equation to predict EQ-5D index scores from the Centers for Disease Control and Prevention’s Healthy Days measures for the New York City (NYC) adult population from 1995 to 2006 and compared these trends over time with the US general population. Such scores enabled us to examine the burden of disease attributable to smoking and overweight/obesity at both the local and national levels. We employed the estimation equation to the 1993–2007 Behavioral Risk Factor Surveillance System (BRFSS) data to obtain EQ-5D index scores for all survey respondents based on their age, self-rated health status, and overall number of unhealthy days. With the combination of mortality data, we calculated trends of quality-adjusted life years (QALYs), life expectancy (LE), and quality-adjusted life expectancy (QALE) as well as the percent of QALYs and QALE lost contributed by smoking and overweight/obesity. Mean EQ-5D index scores for NYC adults decreased from 0.874 to 0.852 but, more recently, have increased to 0.869. The LE of an 18-year-old living in NYC increased 4.7 years and QALE increased 2.6 years. The contribution of smoking to the proportion of QALYs lost decreased from 6.7% to 3.5%, while the contribution of overweight/obesity to the proportion of QALYs lost increased from 4.5% to 16.9%. The proportion of QALEs lost due to smoking decreased from 5.5% to 4.5%, while the proportion of QALEs lost due to overweight/obesity increased from 3.5% to 11.8%. Because the Healthy Days measures have been included in the BRFSS since 1993, translating Healthy Days Measures to a preference-based measure is a useful method for longitudinal tracking of population health at the local, state, and national level.

Background:

Smokers with low body mass index (BMI) may be more susceptible to lung cancer.

Methods:

We prospectively examined the association between baseline BMI and lung cancer risk in the Singapore Chinese Health Study, a cohort of 63 257 Chinese enrolled between 1993 and 1998.

Results:

After adjustment for smoking intensity and duration, BMI was inversely associated with risk of lung cancer among current smokers (P for trend=0.0004). Current smokers at different dosage of smoking with low BMI had significantly higher risk for lung cancer than those with high BMI. Hazard ratios (95% confidence intervals) of lung cancer for heavy smokers with BMI of ⩾28, 24–<28, 20–<24, and <20 kg m−2 were 6.37 (2.10–19.30), 9.01 (5.04–16.10), 8.53 (6.35–11.5), and 11.12 (6.60–18.70), respectively, as compared with nonsmokers. BMI had no modifying effects on lung cancer risk among nonsmokers and former smokers.

Conclusion:

Smokers with lower BMI may experience an enhanced risk of lung cancer. The findings have significant public-health implication given the increase in smoking prevalence in developing countries, where people still have relatively low BMI.

Background

The medico-economic impact of smoking cessation considering a smoking patient with chronic obstructive pulmonary disease (COPD) is poorly documented.

Objective

Here, considering a COPD smoking patient, the specific burden of continuous smoking was estimated, as well as the effectiveness and the cost-effectiveness of smoking cessation.

Methods

A multi-state Markov model adopting society's perspective was developed. Simulated cohorts of English COPD patients who are active smokers (all severity stages combined or patients with the same initial severity stage) were compared to identical cohorts of patients who quit smoking at cohort initialization. Life expectancy, quality adjusted life-years (QALY), disease-related costs, and incremental cost-effectiveness ratio (ICER: £/QALY) were estimated, considering smoking cessation programs with various possible scenarios of success rates and costs. Sensitivity analyses included the variation of model key parameters.

Principal Findings

At the horizon of a smoking COPD patient's remaining lifetime, smoking cessation at cohort intitialization, relapses being allowed as observed in practice, would result in gains (mean) of 1.27 life-years and 0.68 QALY, and induce savings of −1824 £/patient in the disease-related costs. The corresponding ICER was −2686 £/QALY. Smoking cessation resulted in 0.72, 0.69, 0.64 and 0.42 QALY respectively gained per mild, moderate, severe, and very severe COPD patient, but was nevertheless cost-effective for mild to severe COPD patients in most scenarios, even when hypothesizing expensive smoking cessation intervention programmes associated with low success rates. Considering a ten-year time horizon, the burden of continuous smoking in English COPD patients was estimated to cost a total of 1657 M£ while 452516 QALY would be simultaneously lost.

Conclusions

The study results are a useful support for the setting of smoking cessation programmes specifically targeted to COPD patients.

BACKGROUND

Smoking, alcohol use, and obesity appear to increase the risk of developing non-Hodgkin lymphoma (NHL), but few studies have assessed their impact on NHL prognosis.

METHODS

We evaluated the association of pre-diagnosis cigarette smoking, alcohol use, and body mass index (BMI) on overall survival in 1,286 patients enrolled through population-based registries in the United States from 1998–2000. Hazard Ratios (HR) and 95% confidence intervals (CI) were estimated using Cox regression, adjusting for clinical and demographic factors.

RESULTS

Through 2007, 442 patients died (34%), and the median follow-up on living patients was 7.7 years. Compared to never smokers, former (HR=1.59; 95% CI 1.12–2.26) and current (HR=1.50; 95% CI 0.97–2.29) smokers had poorer survival, and poorer survival was positively associated with smoking duration, number of cigarettes smoked per day, pack-years of smoking, and shorter time since quitting (all p-trend<0.01). Alcohol use was associated with poorer survival (p-trend=0.03); compared to non-users, those drinking more than 43.1 grams/week (median of intake among drinkers) had poorer survival (HR=1.55; 95% CI 1.06–2.27) while those drinkers consuming less than this amount showed no survival disadvantage (HR=1.13; 95% CI 0.75–1.71). Greater body mass index was associated with poorer survival (p-trend=0.046), but the survival disadvantage was only seen among obese individuals (HR=1.32 for BMI ≥30 versus 20–24.9 kg/m2; 95% CI 1.02–1.70). These results held for lymphoma-specific survival and were broadly similar for DLBCL and follicular lymphoma.

CONCLUSIONS

NHL patients who smoked, consumed alcohol or were obese prior to diagnosis had a poorer overall and lymphoma-specific survival.

Background

Body mass index (BMI) data usually have skewed distributions, for which common statistical modeling approaches such as simple linear or logistic regression have limitations.

Methods

Different regression approaches to predict childhood BMI by goodness-of-fit measures and means of interpretation were compared including generalized linear models (GLMs), quantile regression and Generalized Additive Models for Location, Scale and Shape (GAMLSS). We analyzed data of 4967 children participating in the school entry health examination in Bavaria, Germany, from 2001 to 2002. TV watching, meal frequency, breastfeeding, smoking in pregnancy, maternal obesity, parental social class and weight gain in the first 2 years of life were considered as risk factors for obesity.

Results

GAMLSS showed a much better fit regarding the estimation of risk factors effects on transformed and untransformed BMI data than common GLMs with respect to the generalized Akaike information criterion. In comparison with GAMLSS, quantile regression allowed for additional interpretation of prespecified distribution quantiles, such as quantiles referring to overweight or obesity. The variables TV watching, maternal BMI and weight gain in the first 2 years were directly, and meal frequency was inversely significantly associated with body composition in any model type examined. In contrast, smoking in pregnancy was not directly, and breastfeeding and parental social class were not inversely significantly associated with body composition in GLM models, but in GAMLSS and partly in quantile regression models. Risk factor specific BMI percentile curves could be estimated from GAMLSS and quantile regression models.

Conclusion

GAMLSS and quantile regression seem to be more appropriate than common GLMs for risk factor modeling of BMI data.

Background

The estimated gap in life expectancy (LE) between Indigenous and non-Indigenous Australians was 12 years for men and 10 years for women, whereas the Northern Territory Indigenous LE gap was at least 50% greater than the national figures. This study aims to explain the Indigenous LE gap by common modifiable risk factors.

Methods

This study covered the period from 1986 to 2005. Unit record death data from the Northern Territory were used to assess the differences in LE at birth between the Indigenous and non-Indigenous populations by socioeconomic disadvantage, smoking, alcohol abuse, obesity, pollution, and intimate partner violence. The population attributable fractions were applied to estimate the numbers of deaths associated with the selected risks. The standard life table and cause decomposition technique was used to examine the individual and joint effects on health inequality.

Results

The findings from this study indicate that among the selected risk factors, socioeconomic disadvantage was the leading health risk and accounted for one-third to one-half of the Indigenous LE gap. A combination of all six selected risks explained over 60% of the Indigenous LE gap.

Conclusions

Improving socioeconomic status, smoking cessation, and overweight reduction are critical to closing the Indigenous LE gap. This paper presents a useful way to explain the impact of risk factors of health inequalities, and suggests that reducing poverty should be placed squarely at the centre of the strategies to close the Indigenous LE gap.

Background

Smokers usually have a lower Body Mass Index (BMI) when compared to non-smokers. Such a relationship, however, has not been fully studied in obese and morbidly obese patients. The objective of this study was to evaluate the relationship between smoking and BMI among obese and morbidly obese subjects.

Methods

In a case-control study design, 1022 individuals of both genders, 18-65 years of age, were recruited and grouped according to their smoking status (smokers, ex-smokers, and non-smokers) and nutritional state according to BMI (normal weight, overweight, obese, and morbidly obese).

Results

No significant differences were detected in the four BMI groups with respect to smoking status. However, there was a trend towards a higher frequency of smokers among the overweight, obese, and morbidly obese subjects compared to normal weight individuals (p = 0.078). In a logistic regression, after adjusting for potential confounders, morbidly obese subjects had an adjusted OR of 2.25 (95% CI, 1.52-3.34; p < 0.001) to be a smoker when compared to normal weight individuals.

Discussion

In this sample, while the frequency of smokers diminished in normal weight subjects as the BMI increased, such a trend was reversed in overweight, obese, and morbidly obese patients. In the latter group, the prevalence of smokers was significantly higher compared to the other groups. A patient with morbid obesity had a 2-fold increased risk of becoming a smoker. We speculate that these finding could be a consequence of various overlapping risk behaviors because these patients also are generally less physically active and prefer a less healthy diet, in addition to having a greater alcohol intake in relation to their counterparts. The external validity of these findings must be confirmed.

OBJECTIVES:

The aims of the present study were to estimate the prevalence of second-hand smoke exposure in Canada, to identify sociodemographic risk factors for second-hand smoke exposure, and to examine the relationship between second-hand smoke exposure and respiratory and cardiovascular diseases.

METHODS:

Data from the 2000/2001 Statistics Canada Canadian Community Health Survey (n=130,880, aged 12 years or older) were analyzed. Second-hand smoke exposure was based on self-report within the past month. The presence of chronic health conditions was also based on self-report. Because ex-smokers would be expected a priori to have poorer health than never-smokers, the analysis was stratified by previous smoking status.

RESULTS:

Approximately 25% of never-smokers and 30% of ex-smokers self-reported recent second-hand smoke exposure. The following factors were identified as risk factors for second-hand smoke exposure: men; residences in Quebec, Atlantic Canada and the Territories; younger ages; nonimmigrant status; low education and income levels; social assistance receipt; and households without children younger than 12 years of age. After controlling for potential confounders, both never- and ex-smokers exposed to second-hand smoke had significantly higher odds of self-reporting asthma (20% to 30%) and chronic bronchitis (50%) than those not exposed to secondhand smoke. Among ex-smokers, those exposed to second-hand smoke also had significantly higher odds of self-reporting hypertension (20%) than those not exposed to second-hand smoke. No associations were observed between second-hand smoke exposure and emphysema or heart disease.

CONCLUSIONS:

Self-reported recent second-hand smoke exposure in Canada in 2000/2001 was high, and was associated with asthma, chronic bronchitis and hypertension in never- and ex-smokers. Potential causal associations and public health implications warrant additional research.

Background

Mortality from cardiovascular and other chronic diseases has increased in Iran. Our aim was to estimate the effects of smoking and high systolic blood pressure (SBP), fasting plasma glucose (FPG), total cholesterol (TC), and high body mass index (BMI) on mortality and life expectancy, nationally and subnationally, using representative data and comparable methods.

Methods

We used data from the Non-Communicable Disease Surveillance Survey to estimate means and standard deviations for the metabolic risk factors, nationally and by region. Lung cancer mortality was used to measure cumulative exposure to smoking. We used data from the death registration system to estimate age-, sex-, and disease-specific numbers of deaths in 2005, adjusted for incompleteness using demographic methods. We used systematic reviews and meta-analyses of epidemiologic studies to obtain the effect of risk factors on disease-specific mortality. We estimated deaths and life expectancy loss attributable to risk factors using the comparative risk assessment framework.

Results

In 2005, high SBP was responsible for 41,000 (95% uncertainty interval: 38,000, 44,000) deaths in men and 39,000 (36,000, 42,000) deaths in women in Iran. High FPG, BMI, and TC were responsible for about one-third to one-half of deaths attributable to SBP in men and/or women. Smoking was responsible for 9,000 deaths among men and 2,000 among women. If SBP were reduced to optimal levels, life expectancy at birth would increase by 3.2 years (2.6, 3.9) and 4.1 years (3.2, 4.9) in men and women, respectively; the life expectancy gains ranged from 1.1 to 1.8 years for TC, BMI, and FPG. SBP was also responsible for the largest number of deaths in every region, with age-standardized attributable mortality ranging from 257 to 333 deaths per 100,000 adults in different regions.

Discussion

Management of blood pressure through diet, lifestyle, and pharmacological interventions should be a priority in Iran. Interventions for other metabolic risk factors and smoking can also improve population health.

Study objective

To study the effect of long‐term smoking on all‐cause and cause‐specific mortality, and to estimate the effects of cigarette and cigar or pipe smoking on life expectancy.

Design

A long‐term prospective cohort study.

Setting

Zutphen, The Netherlands.

Participants

1373 men from the Zutphen Study, born between 1900 and 1920 and studied between 1960 and 2000.

Measurements

Hazard ratios for the type of smoking, amount and duration of cigarette smoking, obtained from a time‐dependent Cox regression model. Absolute health effects of smoking are expressed as differences in life expectancy and the number of disease‐free years of life.

Main results

Duration of cigarette smoking was strongly associated with mortality from cardiovascular disease, lung cancer and chronic obstructive pulmonary disease, whereas both the number of cigarettes smoked as well as duration of cigarette smoking were strongly associated with all‐cause mortality. Average cigarette smoking reduced the total life expectancy by 6.8 years, whereas heavy cigarette smoking reduced the total life expectancy by 8.8 years. The number of total life‐years lost due to cigar or pipe smoking was 4.7 years. Moreover, cigarette smoking reduced the number of disease‐free life‐years by 5.8 years, and cigar or pipe smoking by 5.2 years. Stopping cigarette smoking at age 40 increased the life expectancy by 4.6 years, while the number of disease‐free life‐years was increased by 3.0 years.

Conclusions

Cigar or pipe smoking reduces life expectancy to a lesser extent than cigarette smoking. Both the number of cigarettes smoked and duration of smoking are strongly associated with mortality risk and the number of life‐years lost. Stopping smoking after age 40 has major health benefits.

Obesity and cigarette smoking are often cited separately as the top two preventable causes of death in the US; however, little research has explored factors associated with being both obese and a smoker. Delay discounting is a behavioral characteristic that may underlie both of these conditions/behaviors. Delay discounting describes the extent to which an individual discounts the value of an outcome because of a delay to its occurrence. Higher rates of discounting are often considered an index of impulsivity and have been linked with obesity and cigarette smoking. No research to date has explored delay discounting in a sample obese smokers. For the current study, adolescent smokers classified as obese (BMI greater than 95th percentile) or healthy-weight (BMI between the 5th and 85th percentiles) were compared on a laboratory assessment of delay discounting. Obese smokers discounted significantly more by delay than healthy-weight smokers. This difference remained statistically significant even after controlling for demographic variables that differed across groups. These findings suggest that the relationships between delay discounting and obesity and cigarette smoking may be additive, such that extreme discounting might proportionally increase risk of becoming an obese smoker. However, future prospective work is needed to fully determine the veracity of this hypothesis.

Hypertension can lead to cardiovascular diseases and other chronic conditions. While the impact of hypertension on premature death and life expectancy has been published, the impact on health-adjusted life expectancy has not, and constitutes the research objective of this study. Health-adjusted life expectancy (HALE) is the number of expected years of life equivalent to years lived in full health. Data were obtained from the Canadian Chronic Disease Surveillance System (mortality data 2004–2006) and the Canadian Community Health Survey (Health Utilities Index data 2000–2005) for people with and without hypertension. Life table analysis was applied to calculate life expectancy and health-adjusted life expectancy and their confidence intervals. Our results show that for Canadians 20 years of age, without hypertension, life expectancy is 65.4 years and 61.0 years, for females and males, respectively. HALE is 55.0 years and 52.8 years for the two sexes at age 20; and 24.7 years and 22.9 years at age 55. For Canadians with hypertension, HALE is only 48.9 years and 47.1 years for the two sexes at age 20; and 22.7 years and 20.2 years at age 55. Hypertension is associated with a significant loss in health-adjusted life expectancy compared to life expectancy.

Although underweight and obesity have been associated with increased risk of mortality, it remains unclear whether the associations differ by hormone therapy (HT) use and smoking. The authors examined the relationship between body mass index (BMI) and mortality within the California Teachers Study (CTS), specifically considering the impact of hormone therapy (HT) and smoking. The authors examined the associations of underweight and obesity with risks of all-cause and cause-specific mortality, among 115,433 women participating in the CTS, and specifically examined whether HT use or smoking modifies the effects of obesity. Multivariable Cox proportional hazards regression provided estimates of relative risks (RRs) and 95% confidence intervals (CIs). During follow up, 10,574 deaths occurred. All-cause mortality was increased for underweight (BMI < 18.5; adjusted relative risk [RR] = 1.33, 95% confidence interval [CI] =1.20–1.47) and obese participants (BMI ≥ 30: RR = 1.27, 95% CI = 1.19–1.37) relative to BMI of 18.5 – 24.9). Respiratory disease mortality was increased for underweight and obese participants. Death from any cancer, and breast cancer specifically, and cardiovascular disease was observed only for obese participants. The obesity and mortality association remained after stratification on HT and smoking. Obese participants remained at greater risk for mortality after stratification on menopausal hormone therapy and smoking. Obesity was associated with increased all-cause mortality, as well as death from any cancer (including breast), and cardiovascular and respiratory diseases. These findings help to identify groups at risk for BMI-related poor health outcomes.

Background

Esophageal adenocarcinoma (EA) incidence in many developed countries has increased dramatically over four decades, while survival remains poor. Persons with Barrett's esophagus (BE), who experience substantially elevated EA risk, are typically followed in surveillance involving periodic endoscopy with biopsies, although few progress to EA. No medical, surgical or lifestyle interventions have been proven to safely lower EA risk.

Design

We investigated whether smoking, obesity or alcohol could predict progression to EA in a prospective cohort of 411 BE patients. Data were collected during personal interview. Adjusted hazard ratios (HR) were estimated using Cox regression.

Results

39% had body mass index (BMI) over 30 and 64% had smoked cigarettes. Main analyses focused on those with at least 5 months of follow-up (33,635 person-months), in whom 45 developed EA. Risk increased by 3% per year of age (trend p-value 0.02), with approximate doubling of risk among males. EA risk increased with smoking pack-years (trend p-value 0.04) and duration (p-value 0.05). Compared to never-smokers, the HR for those in the highest pack-year tertile was 2.29 (95%CI 1.04–5.07). No association was found with alcohol or BMI, whereas a suggestion of increased risk was observed in those with higher waist-hip ratio, especially among males.

Conclusion

EA risk significantly increased with increasing age and cigarette exposure. Abdominal obesity, but not BMI, was associated with a modest increased risk. Continued follow-up of this and other cohorts is needed to precisely define these relationships so as to inform risk stratification and preventive interventions.