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1.  Comparing life expectancy and health-adjusted life expectancy by body mass index category in adult Canadians: a descriptive study 
Background
While many studies have examined differences between body mass index (BMI) categories in terms of mortality risk and health-related quality of life (HRQL), little is known about the effect of body weight on health expectancy. We examined life expectancy (LE), health-adjusted life expectancy (HALE), and proportion of LE spent in nonoptimal (or poor) health by BMI category for the Canadian adult population (age ≥ 20).
Methods
Respondents to the National Population Health Survey (NPHS) were followed for mortality outcomes from 1994 to 2009. Our study population at baseline (n=12,478) was 20 to 100 years old with an average age of 47. LE was produced by building abridged life tables by sex and BMI category using data from the NPHS and the Canadian Chronic Disease Surveillance System. HALE was estimated using the Health Utilities Index from the Canadian Community Health Survey as a measure of HRQL. The contribution of HRQL to loss of healthy life years for each BMI category was also assessed using two methods: by calculating differences between LE and HALE proportional to LE and by using a decomposition technique to separate out mortality and HRQL contributions to loss of HALE.
Results
At age 20, for both sexes, LE is significantly lower in the underweight and obesity class 2+ categories, but significantly higher in the overweight category when compared to normal weight (obesity class 1 was nonsignificant). HALE at age 20 follows these same associations and is significantly lower for class 1 obesity in women. Proportion of life spent in nonoptimal health and decomposition of HALE demonstrate progressively higher losses of healthy life associated with lowered HRQL for BMI categories in excess of normal weight.
Conclusions
Although being in the overweight category for adults may be associated with a gain in life expectancy as compared to normal weight adults, overweight individuals also experience a higher proportion of these years of life in poorer health. Due to the descriptive nature of this study, further research is needed to explore the causal mechanisms which explain these results, including the important differences we observed between sexes and within obesity subcategories.
doi:10.1186/1478-7954-11-21
PMCID: PMC3842774  PMID: 24252500
Overweight; Obesity; Underweight; Body mass index; Life expectancy; Health expectancy; Mortality; Health-related quality of life
2.  Lifetime Medical Costs of Obesity: Prevention No Cure for Increasing Health Expenditure 
PLoS Medicine  2008;5(2):e29.
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)
doi:10.1371/journal.pmed.0050029
PMCID: PMC2225430  PMID: 18254654
3.  Association between Class III Obesity (BMI of 40–59 kg/m2) and Mortality: A Pooled Analysis of 20 Prospective Studies 
PLoS Medicine  2014;11(7):e1001673.
In a pooled analysis of 20 prospective studies, Cari Kitahara and colleagues find that class III obesity (BMI of 40–59) is associated with excess rates of total mortality, particularly due to heart disease, cancer, and diabetes.
Please see later in the article for the Editors' Summary
Background
The prevalence of class III obesity (body mass index [BMI]≥40 kg/m2) has increased dramatically in several countries and currently affects 6% of adults in the US, with uncertain impact on the risks of illness and death. Using data from a large pooled study, we evaluated the risk of death, overall and due to a wide range of causes, and years of life expectancy lost associated with class III obesity.
Methods and Findings
In a pooled analysis of 20 prospective studies from the United States, Sweden, and Australia, we estimated sex- and age-adjusted total and cause-specific mortality rates (deaths per 100,000 persons per year) and multivariable-adjusted hazard ratios for adults, aged 19–83 y at baseline, classified as obese class III (BMI 40.0–59.9 kg/m2) compared with those classified as normal weight (BMI 18.5–24.9 kg/m2). Participants reporting ever smoking cigarettes or a history of chronic disease (heart disease, cancer, stroke, or emphysema) on baseline questionnaires were excluded. Among 9,564 class III obesity participants, mortality rates were 856.0 in men and 663.0 in women during the study period (1976–2009). Among 304,011 normal-weight participants, rates were 346.7 and 280.5 in men and women, respectively. Deaths from heart disease contributed largely to the excess rates in the class III obesity group (rate differences = 238.9 and 132.8 in men and women, respectively), followed by deaths from cancer (rate differences = 36.7 and 62.3 in men and women, respectively) and diabetes (rate differences = 51.2 and 29.2 in men and women, respectively). Within the class III obesity range, multivariable-adjusted hazard ratios for total deaths and deaths due to heart disease, cancer, diabetes, nephritis/nephrotic syndrome/nephrosis, chronic lower respiratory disease, and influenza/pneumonia increased with increasing BMI. Compared with normal-weight BMI, a BMI of 40–44.9, 45–49.9, 50–54.9, and 55–59.9 kg/m2 was associated with an estimated 6.5 (95% CI: 5.7–7.3), 8.9 (95% CI: 7.4–10.4), 9.8 (95% CI: 7.4–12.2), and 13.7 (95% CI: 10.5–16.9) y of life lost. A limitation was that BMI was mainly ascertained by self-report.
Conclusions
Class III obesity is associated with substantially elevated rates of total mortality, with most of the excess deaths due to heart disease, cancer, and diabetes, and major reductions in life expectancy compared with normal weight.
Please see later in the article for the Editors' Summary
Editors' Summary
Background
The number of obese people (individuals with an excessive amount of body fat) is increasing rapidly in many countries. Worldwide, according to the Global Burden of Disease Study 2013, more than a third of all adults are now overweight or obese. Obesity is defined as having a body mass index (BMI, an indicator of body fat calculated by dividing a person's weight in kilograms by their height in meters squared) of more than 30 kg/m2 (a 183-cm [6-ft] tall man who weighs more than 100 kg [221 lbs] is obese). Compared to people with a healthy weight (a BMI between 18.5 and 24.9 kg/m2), overweight and obese individuals (who have a BMI between 25.0 and 29.9 kg/m2 and a BMI of 30 kg/m2 or more, respectively) have an increased risk of developing diabetes, heart disease, stroke, and some cancers, and tend to die younger. Because people become unhealthily fat by consuming food and drink that contains more energy (kilocalories) than they need for their daily activities, obesity can be prevented or treated by eating less food and by increasing physical activity.
Why Was This Study Done?
Class III obesity (extreme, or morbid, obesity), which is defined as a BMI of more than 40 kg/m2, is emerging as a major public health problem in several high-income countries. In the US, for example, 6% of adults are now morbidly obese. Because extreme obesity used to be relatively uncommon, little is known about the burden of disease, including total and cause-specific mortality (death) rates, among individuals with class III obesity. Before we can prevent and treat class III obesity effectively, we need a better understanding of the health risks associated with this condition. In this pooled analysis of prospective cohort studies, the researchers evaluate the risk of total and cause-specific death and the years of life lost associated with class III obesity. A pooled analysis analyzes the data from several studies as if the data came from one large study; prospective cohort studies record the characteristics of a group of participants at baseline and follow them to see which individuals develop a specific condition.
What Did the Researchers Do and Find?
The researchers included 20 prospective (mainly US) cohort studies from the National Cancer Institute Cohort Consortium (a partnership that studies cancer by undertaking large-scale collaborations) in their pooled analysis. After excluding individuals who had ever smoked and people with a history of chronic disease, the analysis included 9,564 adults who were classified as class III obese based on self-reported height and weight at baseline and 304,011 normal-weight adults. Among the participants with class III obesity, mortality rates (deaths per 100,000 persons per year) during the 30-year study period were 856.0 and 663.0 in men and women, respectively, whereas the mortality rates among normal-weight men and women were 346.7 and 280.5, respectively. Heart disease was the major contributor to the excess death rate among individuals with class III obesity, followed by cancer and diabetes. Statistical analyses of the pooled data indicate that the risk of all-cause death and death due to heart disease, cancer, diabetes, and several other diseases increased with increasing BMI. Finally, compared with having a normal weight, having a BMI between 40 and 59 kg/m2 resulted in an estimated loss of 6.5 to 13.7 years of life.
What Do These Findings Mean?
These findings indicate that class III obesity is associated with a substantially increased rate of death. Notably, this death rate increase is similar to the increase associated with smoking among normal-weight people. The findings also suggest that heart disease, cancer, and diabetes are responsible for most of the excess deaths among people with class III obesity and that having class III obesity results in major reductions in life expectancy. Importantly, the number of years of life lost continues to increase for BMI values above 50 kg/m2, and beyond this point, the loss of life expectancy exceeds that associated with smoking among normal-weight people. The accuracy of these findings is limited by the use of self-reported height and weight measurements to calculate BMI and by the use of BMI as the sole measure of obesity. Moreover, these findings may not be generalizable to all populations. Nevertheless, these findings highlight the need to develop more effective interventions to combat the growing public health problem of class III obesity.
Additional Information
Please access these websites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.1001673.
The US Centers for Disease Control and Prevention provides information on all aspects of overweight and obesity (in English and Spanish)
The World Health Organization provides information on obesity (in several languages); Malri's story describes the health risks faced by an obese child
The UK National Health Service Choices website provides information about obesity, including a personal story about losing weight
The Global Burden of Disease Study website provides the latest details about global obesity trends
The US Department of Agriculture's ChooseMyPlate.gov website provides a personal healthy eating plan; the Weight-Control Information Network is an information service provided for the general public and health professionals by the US National Institute of Diabetes and Digestive and Kidney Diseases (in English and Spanish)
MedlinePlus provides links to other sources of information on obesity (in English and Spanish)
doi:10.1371/journal.pmed.1001673
PMCID: PMC4087039  PMID: 25003901
4.  Impact of diabetes mellitus on life expectancy and health-adjusted life expectancy in Canada 
The objectives of this study were to estimate life expectancy (LE) and health-adjusted life expectancy (HALE) for Canadians with and without diabetes and to evaluate the impact of diabetes on population health using administrative and survey data.
Mortality data from the Canadian Chronic Disease Surveillance System (2004 to 2006) and Health Utilities Index data from the Canadian Community Health Survey (2000 to 2005) were used. Life table analysis was applied to calculate LE, HALE, and their confidence intervals using the Chiang and the adapted Sullivan methods.
LE and HALE were significantly lower among people with diabetes than for people without the disease. LE and HALE for females without diabetes were 85.0 and 73.3 years, respectively (males: 80.2 and 70.9 years). Diabetes was associated with a loss of LE and HALE of 6.0 years and 5.8 years, respectively, for females, and 5.0 years and 5.3 years, respectively, for males, living with diabetes at 55 years of age. The overall gains in LE and HALE after the hypothetical elimination of prevalent diagnosed diabetes cases in the population were 1.4 years and 1.2 years, respectively, for females, and 1.3 years for both LE and HALE for males.
The results of the study confirm that diabetes is an important disease burden in Canada impacting the female and male populations differently. The methods can be used to calculate LE and HALE for other chronic conditions, providing useful information for public health researchers and policymakers.
doi:10.1186/1478-7954-10-7
PMCID: PMC3787852  PMID: 22531113
Life expectancy; Health-adjusted life expectancy; Diabetes mellitus; Health utilities index; Summary measure of population health
5.  Disability weights for comorbidity and their influence on Health-adjusted Life Expectancy 
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.
doi:10.1186/1478-7954-4-1
PMCID: PMC1523368  PMID: 16606448
6.  Adjusting for dependent comorbidity in the calculation of healthy life expectancy 
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.
doi:10.1186/1478-7954-4-4
PMCID: PMC1484491  PMID: 16620383
7.  Self-rated health as a tool for estimating health-adjusted life expectancy among patients newly diagnosed with localized prostate cancer: A preliminary study 
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.
doi:10.1007/s11136-010-9805-3
PMCID: PMC3066264  PMID: 21132389
Localized Prostate Cancer; Life Expectancy; Self-rated Health; Urology
8.  The Promise of Prevention: The Effects of Four Preventable Risk Factors on National Life Expectancy and Life Expectancy Disparities by Race and County in the United States 
PLoS Medicine  2010;7(3):e1000248.
Majid Ezzati and colleagues examine the contribution of a set of risk factors (smoking, high blood pressure, elevated blood glucose, and adiposity) to socioeconomic disparities in life expectancy in the US population.
Background
There has been substantial research on psychosocial and health care determinants of health disparities in the United States (US) but less on the role of modifiable risk factors. We estimated the effects of smoking, high blood pressure, elevated blood glucose, and adiposity on national life expectancy and on disparities in life expectancy and disease-specific mortality among eight subgroups of the US population (the “Eight Americas”) defined on the basis of race and the location and socioeconomic characteristics of county of residence, in 2005.
Methods and Findings
We combined data from the National Health and Nutrition Examination Survey and the Behavioral Risk Factor Surveillance System to estimate unbiased risk factor levels for the Eight Americas. We used data from the National Center for Health Statistics to estimate age–sex–disease-specific number of deaths in 2005. We used systematic reviews and meta-analyses of epidemiologic studies to obtain risk factor effect sizes for disease-specific mortality. We used epidemiologic methods for multiple risk factors to estimate the effects of current exposure to these risk factors on death rates, and life table methods to estimate effects on life expectancy. Asians had the lowest mean body mass index, fasting plasma glucose, and smoking; whites had the lowest systolic blood pressure (SBP). SBP was highest in blacks, especially in the rural South—5–7 mmHg higher than whites. The other three risk factors were highest in Western Native Americans, Southern low-income rural blacks, and/or low-income whites in Appalachia and the Mississippi Valley. Nationally, these four risk factors reduced life expectancy at birth in 2005 by an estimated 4.9 y in men and 4.1 y in women. Life expectancy effects were smallest in Asians (M, 4.1 y; F, 3.6 y) and largest in Southern rural blacks (M, 6.7 y; F, 5.7 y). Standard deviation of life expectancies in the Eight Americas would decline by 0.50 y (18%) in men and 0.45 y (21%) in women if these risks had been reduced to optimal levels. Disparities in the probabilities of dying from cardiovascular diseases and diabetes at different ages would decline by 69%–80%; the corresponding reduction for probabilities of dying from cancers would be 29%–50%. Individually, smoking and high blood pressure had the largest effect on life expectancy disparities.
Conclusions
Disparities in smoking, blood pressure, blood glucose, and adiposity explain a significant proportion of disparities in mortality from cardiovascular diseases and cancers, and some of the life expectancy disparities in the US.
Please see later in the article for the Editors' Summary
Editors' Summary
Background
Life expectancy (a measure of longevity and premature death) and overall health have increased steadily in the United States over recent years. New drugs, new medical technologies, and better disease prevention have all helped Americans to lead longer, healthier lives. However, even now, some Americans live much longer and much healthier lives than others. Health disparities—differences in how often certain diseases occur and cause death in groups of people classified according to their ethnicity, geographical location, sex, or age—are extremely large and persistent in the US. On average, black men and women in the US live 6.3 and 4.5 years less, respectively, than their white counterparts; the gap between life expectancy in the US counties with the lowest and highest life expectancies is 18.4 years for men and 14.3 years for women. Disparities in deaths (mortality) from chronic diseases such as cardiovascular diseases (for example, heart attacks and stroke), cancers, and diabetes are known to be the main determinants of these life expectancy disparities.
Why Was This Study Done?
Preventable risk factors such as smoking, high blood pressure, excessive body fat (adiposity), and high blood sugar are responsible for many thousands of deaths from chronic diseases. Exposure to these risk factors varies widely by race, state of residence, and socioeconomic status. However, the effects of these observed disparities in exposure to modifiable risk factors on US life expectancy disparities have only been examined in selected groups of people and it is not known how multiple modifiable risk factors affect US health disparities. A better knowledge about how disparities in risk factor exposure contribute to health disparities is needed to ensure that prevention programs not only improve the average health status but also reduce health disparities. In this study, the researchers estimate the effects of smoking, high blood pressure, high blood sugar, and adiposity on US life expectancy and on disparities in life expectancy and disease-specific deaths among the “Eight Americas,” population groups defined by race and by the location and socioeconomic characteristics of their county of residence.
What Did the Researchers Do and Find?
The researchers extracted data on exposure to these risk factors from US national health surveys, information on deaths from different diseases in 2005 from the US National Center for Health Statistics, and estimates of how much each risk factor increases the risk of death from each disease from published studies. They then used modeling methods to estimate the effects of risk factor exposure on death rates and life expectancy. The Asian subgroup had the lowest adiposity, blood sugar, and smoking rates, they report, and the three white subgroups had the lowest blood pressure. Blood pressure was highest in the three black subgroups, whereas the other three risk factors were highest in Western Native Americans, Southern rural blacks, and whites living in Appalachia and the Mississippi Valley. The effects on life expectancy of these factors were smallest in Asians and largest in Southern rural blacks but, overall, these risk factors reduced the life expectancy for men and women born in 2005 by 4.9 and 4.1 years, respectively. Other calculations indicate that if these four risk factors were reduced to optimal levels, disparities among the subgroups in deaths from cardiovascular diseases and diabetes and from cancers would be reduced by up to 80% and 50%, respectively.
What Do These Findings Mean?
These findings suggest that disparities in smoking, blood pressure, blood sugar, and adiposity among US racial and geographical subgroups explain a substantial proportion of the disparities in deaths from cardiovascular diseases, diabetes, and cancers among these subgroups. The disparities in risk factor exposure also explain some of the disparities in life expectancy. The remaining disparities in deaths and life expectancy could be the result of preventable risk factors not included in this study—one of its limitations is that it does not consider the effect of dietary fat, alcohol use, and dietary salt, which are major contributors to different diseases. Thus, suggest the researchers, reduced exposure to preventable risk factors through the implementation of relevant policies and programs should reduce life expectancy and mortality disparities in the US and yield health benefits at a national scale.
Additional Information
Please access these Web sites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.1000248.
The US Centers for Disease Control and Prevention, the US Office of Minority Health, and the US National Center on Minority Health and Health Disparities all provide information on health disparities in the US
MedlinePlus provides links to information on health disparities and on healthy living (in English and Spanish)
The US Centers for Disease Control and Prevention provides information on all aspects of healthy living
The American Heart Association and the American Cancer Society provide information on modifiable risk factors for patients and caregivers
Healthy People 2010 is a national framework designed to improve the health of people living in the US
The US National Health and Nutrition Examination Survey (NHANES) and the Behavioral Risk Factor Surveillance System (BRFSS) collect information on risk factor exposures in the US
doi:10.1371/journal.pmed.1000248
PMCID: PMC2843596  PMID: 20351772
9.  Adult Mortality Attributable to Preventable Risk Factors for Non-Communicable Diseases and Injuries in Japan: A Comparative Risk Assessment 
PLoS Medicine  2012;9(1):e1001160.
Using a combination of published data and modeling, Nayu Ikeda and colleagues identify tobacco smoking and high blood pressure as major risk factors for death from noncommunicable diseases among adults in Japan.
Background
The population of Japan has achieved the longest life expectancy in the world. To further improve population health, consistent and comparative evidence on mortality attributable to preventable risk factors is necessary for setting priorities for health policies and programs. Although several past studies have quantified the impact of individual risk factors in Japan, to our knowledge no study has assessed and compared the effects of multiple modifiable risk factors for non-communicable diseases and injuries using a standard framework. We estimated the effects of 16 risk factors on cause-specific deaths and life expectancy in Japan.
Methods and Findings
We obtained data on risk factor exposures from the National Health and Nutrition Survey and epidemiological studies, data on the number of cause-specific deaths from vital records adjusted for ill-defined codes, and data on relative risks from epidemiological studies and meta-analyses. We applied a comparative risk assessment framework to estimate effects of excess risks on deaths and life expectancy at age 40 y. In 2007, tobacco smoking and high blood pressure accounted for 129,000 deaths (95% CI: 115,000–154,000) and 104,000 deaths (95% CI: 86,000–119,000), respectively, followed by physical inactivity (52,000 deaths, 95% CI: 47,000–58,000), high blood glucose (34,000 deaths, 95% CI: 26,000–43,000), high dietary salt intake (34,000 deaths, 95% CI: 27,000–39,000), and alcohol use (31,000 deaths, 95% CI: 28,000–35,000). In recent decades, cancer mortality attributable to tobacco smoking has increased in the elderly, while stroke mortality attributable to high blood pressure has declined. Life expectancy at age 40 y in 2007 would have been extended by 1.4 y for both sexes (men, 95% CI: 1.3–1.6; women, 95% CI: 1.2–1.7) if exposures to multiple cardiovascular risk factors had been reduced to their optimal levels as determined by a theoretical-minimum-risk exposure distribution.
Conclusions
Tobacco smoking and high blood pressure are the two major risk factors for adult mortality from non-communicable diseases and injuries in Japan. There is a large potential population health gain if multiple risk factors are jointly controlled.
Please see later in the article for the Editors' Summary
Editors' Summary
Background
Worldwide, a small number of modifiable risk factors are responsible for many premature or preventable deaths. For example, having high blood pressure (hypertension) increases a person's risk of developing life-threatening heart problems and stroke (cardiovascular disease). Similarly, having a high blood sugar level increases the risk of developing diabetes, a chronic (long-term) disease that can lead to cardiovascular problems and kidney failure, and half of all long-term tobacco smokers in Western populations will die prematurely from diseases related to smoking, such as lung cancer. Importantly, the five major risk factors for death globally—high blood pressure, tobacco use, high blood sugar, physical inactivity, and overweight and obesity—are all modifiable. That is, lifestyle changes and dietary changes such as exercising more, reducing salt intake, and increasing fruit and vegetable intake can reduce an individual's exposure to these risk factors and one's chances of premature death. Moreover, public health programs designed to reduce a population's exposure to modifiable risk factors should reduce preventable deaths in that population.
Why Was This Study Done?
In 2000, the Japanese government initiated Health Japan 21, a ten-year national health promotion campaign designed to prevent premature death from non-communicable (noninfectious) diseases and injuries. This campaign set 59 goals to monitor and improve risk factor management in the Japanese population, which has one of the longest life expectancies at birth in the world (the life expectancy of a person born in Japan in 2009 was 83.1 years). Because the campaign's final evaluation revealed deterioration or no improvement on some of these goals, the Japanese government recently released new guidelines that stress the importance of simultaneously controlling multiple risk factors for chronic diseases. However, although several studies have quantified the impacts on life expectancy and cause-specific death of individual modifiable risk factors in Japan, the effects of multiple risk factors have not been assessed. In this study, the researchers use a “comparative risk assessment” framework to estimate the effects of 16 risk factors on cause-specific deaths and life expectancy in Japan. Comparative risk assessment estimates the number of deaths that would be prevented if current distributions of risk factor exposures were changed to hypothetical optimal distributions.
What Did the Researchers Do and Find?
The researchers obtained data on exposure to the selected risk factors from the 2007 Japanese National Health and Nutrition Survey and from epidemiological studies, and information on the number of deaths in 2007 from different diseases from official records. They used published studies to estimate how much each factor increases the risk of death from each disease and then used a mathematical formula to estimate the effects of the risk factors on the number of deaths in Japan and on life expectancy at age 40. In 2007, tobacco smoking and high blood pressure accounted for 129,000 and 104,000 deaths, respectively, in Japan. Physical inactivity accounted for 52,000 deaths, high blood glucose and high dietary salt intake accounted for 34,000 deaths each, and alcohol use for 31,000 deaths. Life expectancy at age 40 in 2007 would have been extended by 1.4 years for both sexes, the researchers estimate, if exposure to multiple cardiovascular risk factors had been reduced to calculated optimal distributions, or by 0.7 years if these risk factors had been reduced to the distributions defined by national guidelines and goals.
What Do These Findings Mean?
These findings identify tobacco smoking and high blood pressure as the major risk factors for death from non-communicable diseases among adults in Japan, a result consistent with previous findings from the US. They also indicate that simultaneous control of multiple risk factors has great potential for producing health gains among the Japanese population. Although the researchers focused on estimating the effect of these risk factors on mortality and did not include illness and disability in this study, these findings nevertheless identify two areas of public health policy that need to be strengthened to improve health, reduce death rates, and increase life expectancy among the Japanese population. First, they highlight the need to reduce tobacco smoking, particularly among men. Second and most importantly, these findings emphasize the need to improve ongoing programs designed to help people manage multiple cardiovascular risk factors, including high blood pressure.
Additional Information
Please access these websites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.1001160.
The US Centers for Disease Control and Prevention provides information on all aspects of healthy living
The World Health Report 2002—Reducing Risks, Promoting Healthy Life provides a global analysis of how healthy life expectancy could be increased
The American Heart Association and the American Cancer Society provide information on many important risk factors for noncommunicable diseases and include some personal stories about keeping healthy
Details about Health Japan 21 are provided by the Japanese Ministry of Health, Labour and Welfare. Further details about this campaign are available from the World Health Organization
MedlinePlus provides links to further resources on healthy living and on healthy aging (in English and Spanish)
doi:10.1371/journal.pmed.1001160
PMCID: PMC3265534  PMID: 22291576
10.  Using linked data to calculate summary measures of population health: Health-adjusted life expectancy of people with Diabetes Mellitus 
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.
doi:10.1186/1478-7954-2-4
PMCID: PMC406424  PMID: 15038828
11.  Bariatric Surgery 
Executive Summary
Objective
To conduct an evidence-based analysis of the effectiveness and cost-effectiveness of bariatric surgery.
Background
Obesity is defined as a body mass index (BMI) of at last 30 kg/m2.1 Morbid obesity is defined as a BMI of at least 40 kg/m2 or at least 35 kg/m2 with comorbid conditions. Comorbid conditions associated with obesity include diabetes, hypertension, dyslipidemias, obstructive sleep apnea, weight-related arthropathies, and stress urinary incontinence. It is also associated with depression, and cancers of the breast, uterus, prostate, and colon, and is an independent risk factor for cardiovascular disease.
Obesity is also associated with higher all-cause mortality at any age, even after adjusting for potential confounding factors like smoking. A person with a BMI of 30 kg/m2 has about a 50% higher risk of dying than does someone with a healthy BMI. The risk more than doubles at a BMI of 35 kg/m2. An expert estimated that about 160,000 people are morbidly obese in Ontario. In the United States, the prevalence of morbid obesity is 4.7% (1999–2000).
In Ontario, the 2004 Chief Medical Officer of Health Report said that in 2003, almost one-half of Ontario adults were overweight (BMI 25–29.9 kg/m2) or obese (BMI ≥ 30 kg/m2). About 57% of Ontario men and 42% of Ontario women were overweight or obese. The proportion of the population that was overweight or obese increased gradually from 44% in 1990 to 49% in 2000, and it appears to have stabilized at 49% in 2003. The report also noted that the tendency to be overweight and obese increases with age up to 64 years. BMI should be used cautiously for people aged 65 years and older, because the “normal” range may begin at slightly above 18.5 kg/m2 and extend into the “overweight” range.
The Chief Medical Officer of Health cautioned that these data may underestimate the true extent of the problem, because they were based on self reports, and people tend to over-report their height and under-report their weight. The actual number of Ontario adults who are overweight or obese may be higher.
Diet, exercise, and behavioural therapy are used to help people lose weight. The goals of behavioural therapy are to identify, monitor, and alter behaviour that does not help weight loss. Techniques include self-monitoring of eating habits and physical activity, stress management, stimulus control, problem solving, cognitive restructuring, contingency management, and identifying and using social support. Relapse, when people resume old, unhealthy behaviour and then regain the weight, can be problematic.
Drugs (including gastrointestinal lipase inhibitors, serotonin norepinephrine reuptake inhibitors, and appetite suppressants) may be used if behavioural interventions fail. However, estimates of efficacy may be confounded by high rates of noncompliance, in part owing to the side effects of the drugs. In addition, the drugs have not been approved for indefinite use, despite the chronic nature of obesity.
The Technology
Morbidly obese people may be eligible for bariatric surgery. Bariatric surgery for morbid obesity is considered an intervention of last resort for patients who have attempted first-line forms of medical management, such as diet, increased physical activity, behavioural modification, and drugs.
There are various bariatric surgical procedures and several different variations for each of these procedures. The surgical interventions can be divided into 2 general types: malabsorptive (bypassing parts of the gastrointestinal tract to limit the absorption of food), and restrictive (decreasing the size of the stomach so that the patient is satiated with less food). All of these may be performed as either open surgery or laparoscopically. An example of a malabsorptive technique is Roux-en-Y gastric bypass (RYGB). Examples of restrictive techniques are vertical banded gastroplasty (VBG) and adjustable gastric banding (AGB).
The Ontario Health Insurance Plan (OHIP) Schedule of Benefits for Physician Services includes fee code “S120 gastric bypass or partition, for morbid obesity” as an insured service. The term gastric bypass is a general term that encompasses a variety of surgical methods, all of which involve reconfiguring the digestive system. The term gastric bypass does not include AGB. The number of gastric bypass procedures funded and done in Ontario, and funded as actual out-of-country approvals,2 is shown below.
Number of Gastric Bypass Procedures by Fiscal Year: Ontario and Actual Out-of-Country (OOC) Approvals
Data from Provider Services, MOHLTC
Courtesy of Provider Services, Ministry of Health and Long Term Care
Review Strategy
The Medical Advisory Secretariat reviewed the literature to assess the effectiveness, safety, and cost-effectiveness of bariatric surgery to treat morbid obesity. It used its standard search strategy to retrieve international health technology assessments and English-language journal articles from selected databases. The interventions of interest were bariatric surgery and, for the controls, either optimal conventional management or another type of bariatric procedure. The outcomes of interest were improvement in comorbid conditions (e.g., diabetes, hypertension); short- and long-term weight loss; quality of life; adverse effects; and economic analysis data. The databases yielded 15 international health technology assessments or systematic reviews on bariatric surgery.
Subsequently, the Medical Advisory Secretariat searched MEDLINE and EMBASE from April 2004 to December 2004, after the search cut-off date of April, 2004, for the most recent systematic reviews on bariatric surgery. Ten studies met the inclusion criteria. One of those 10 was the Swedish Obese Subjects study, which started as a registry and intervention study, and then published findings on people who had been enrolled for at least 2 years or at least 10 years. In addition to the literature review of economic analysis data, the Medical Advisory Secretariat also did an Ontario-based economic analysis.
Summary of Findings
Bariatric surgery generally is effective for sustained weight loss of about 16% for people with BMIs of at least 40 kg/m2 or at least 35 kg/m2 with comorbid conditions (including diabetes, high lipid levels, and hypertension). It also is effective at resolving the associated comorbid conditions. This conclusion is largely based on level 3a evidence from the prospectively designed Swedish Obese Subjects study, which recently published 10-year outcomes for patients who had bariatric surgery compared with patients who received nonsurgical treatment. (1)
Regarding specific procedures, there is evidence that malabsorptive techniques are better than other banding techniques for weight loss and resolution of comorbid illnesses. However, there are no published prospective, long-term, direct comparisons of these techniques available.
Surgery for morbid obesity is considered an intervention of last resort for patients who have attempted first-line forms of medical management, such as diet, increased physical activity, behavioural modification, and drugs. In the absence of direct comparisons of active nonsurgical intervention via caloric restriction with bariatric techniques, the following observations are made:
A recent systematic review examining the efficacy of major commercial and organized self-help weight loss programs in the United States concluded that the evidence to support the use of such programs was suboptimal, except for one trial on Weight Watchers. Furthermore, the programs were associated with high costs, attrition rates, and probability of regaining at least 50% of the lost weight in 1 to 2 years. (2)
A recent randomized controlled trial reported 1-year outcomes comparing weight loss and metabolic changes in severely obese patients assigned to either a low-carbohydrate diet or a conventional weight loss diet. At 1 year, weight loss was similar for patients in each group (mean, 2–5 kg). There was a favourable effect on triglyceride levels and glycemic control in the low-carbohydrate diet group. (3)
A decision-analysis model showed bariatric surgery results in increased life expectancy in morbidly obese patients when compared to diet and exercise. (4)
A cost-effectiveness model showed bariatric surgery is cost-effective relative to nonsurgical management. (5)
Extrapolating from 2003 data from the United States, Ontario would likely need to do 3,500 bariatric surgeries per year. It currently does 508 per year, including out-of-country surgeries.
PMCID: PMC3382415  PMID: 23074460
12.  Comparing population health in the United States and Canada 
Background
The objective of the paper is to compare population health in the United States (US) and Canada. Although the two countries are very similar in many ways, there are potentially important differences in the levels of social and economic inequality and the organization and financing of and access to health care in the two countries.
Methods
Data are from the Joint Canada/United States Survey of Health 2002/03. The Health Utilities Index Mark 3 (HUI3) was used to measure overall health-related quality of life (HRQL). Mean HUI3 scores were compared, adjusting for major determinants of health, including body mass index, smoking, education, gender, race, and income. In addition, estimates of life expectancy were compared. Finally, mean HUI3 scores by age and gender and Canadian and US life tables were used to estimate health-adjusted life expectancy (HALE).
Results
Life expectancy in Canada is higher than in the US. For those < 40 years, there were no differences in HRQL between the US and Canada. For the 40+ group, HRQL appears to be higher in Canada. The results comparing the white-only population in both countries were very similar. For a 19-year-old, HALE was 52.0 years in Canada and 49.3 in the US.
Conclusions
The population of Canada appears to be substantially healthier than the US population with respect to life expectancy, HRQL, and HALE. Factors that account for the difference may include access to health care over the full life span (universal health insurance) and lower levels of social and economic inequality, especially among the elderly.
doi:10.1186/1478-7954-8-8
PMCID: PMC2873793  PMID: 20429875
13.  The Preventable Causes of Death in the United States: Comparative Risk Assessment of Dietary, Lifestyle, and Metabolic Risk Factors 
PLoS Medicine  2009;6(4):e1000058.
Majid Ezzati and colleagues examine US data on risk factor exposures and disease-specific mortality and find that smoking and hypertension, which both have effective interventions, are responsible for the largest number of deaths.
Background
Knowledge of the number of deaths caused by risk factors is needed for health policy and priority setting. Our aim was to estimate the mortality effects of the following 12 modifiable dietary, lifestyle, and metabolic risk factors in the United States (US) using consistent and comparable methods: high blood glucose, low-density lipoprotein (LDL) cholesterol, and blood pressure; overweight–obesity; high dietary trans fatty acids and salt; low dietary polyunsaturated fatty acids, omega-3 fatty acids (seafood), and fruits and vegetables; physical inactivity; alcohol use; and tobacco smoking.
Methods and Findings
We used data on risk factor exposures in the US population from nationally representative health surveys and disease-specific mortality statistics from the National Center for Health Statistics. We obtained the etiological effects of risk factors on disease-specific mortality, by age, from systematic reviews and meta-analyses of epidemiological studies that had adjusted (i) for major potential confounders, and (ii) where possible for regression dilution bias. We estimated the number of disease-specific deaths attributable to all non-optimal levels of each risk factor exposure, by age and sex. In 2005, tobacco smoking and high blood pressure were responsible for an estimated 467,000 (95% confidence interval [CI] 436,000–500,000) and 395,000 (372,000–414,000) deaths, accounting for about one in five or six deaths in US adults. Overweight–obesity (216,000; 188,000–237,000) and physical inactivity (191,000; 164,000–222,000) were each responsible for nearly 1 in 10 deaths. High dietary salt (102,000; 97,000–107,000), low dietary omega-3 fatty acids (84,000; 72,000–96,000), and high dietary trans fatty acids (82,000; 63,000–97,000) were the dietary risks with the largest mortality effects. Although 26,000 (23,000–40,000) deaths from ischemic heart disease, ischemic stroke, and diabetes were averted by current alcohol use, they were outweighed by 90,000 (88,000–94,000) deaths from other cardiovascular diseases, cancers, liver cirrhosis, pancreatitis, alcohol use disorders, road traffic and other injuries, and violence.
Conclusions
Smoking and high blood pressure, which both have effective interventions, are responsible for the largest number of deaths in the US. Other dietary, lifestyle, and metabolic risk factors for chronic diseases also cause a substantial number of deaths in the US.
Please see later in the article for Editors' Summary
Editors' Summary
Background
A number of modifiable factors are responsible for many premature or preventable deaths. For example, being overweight or obese shortens life expectancy, while half of all long-term tobacco smokers in Western populations will die prematurely from a disease directly related to smoking. Modifiable risk factors fall into three main groups. First, there are lifestyle risk factors. These include tobacco smoking, physical inactivity, and excessive alcohol use (small amounts of alcohol may actually prevent diabetes and some types of heart disease and stroke). Second, there are dietary risk factors such as a high salt intake and a low intake of fruits and vegetables. Finally, there are “metabolic risk factors,” which shorten life expectancy by increasing a person's chances of developing cardiovascular disease (in particular, heart problems and strokes) and diabetes. Metabolic risk factors include having high blood pressure or blood cholesterol and being overweight or obese.
Why Was This Study Done?
It should be possible to reduce preventable deaths by changing modifiable risk factors through introducing public health policies, programs and regulations that reduce exposures to these risk factors. However, it is important to know how many deaths are caused by each risk factor before developing policies and programs that aim to improve a nation's health. Although previous studies have provided some information on the numbers of premature deaths caused by modifiable risk factors, there are two problems with these studies. First, they have not used consistent and comparable methods to estimate the number of deaths attributable to different risk factors. Second, they have rarely considered the effects of dietary and metabolic risk factors. In this new study, the researchers estimate the number of deaths due to 12 different modifiable dietary, lifestyle, and metabolic risk factors for the United States population. They use a method called “comparative risk assessment.” This approach estimates the number of deaths that would be prevented if current distributions of risk factor exposures were changed to hypothetical optimal distributions.
What Did the Researchers Do and Find?
The researchers extracted data on exposures to these 12 selected risk factors from US national health surveys, and they obtained information on deaths from difference diseases for 2005 from the US National Center for Health Statistics. They used previously published studies to estimate how much each risk factor increases the risk of death from each disease. The researchers then used a mathematical formula to estimate the numbers of deaths caused by each risk factor. Of the 2.5 million US deaths in 2005, they estimate that nearly half a million were associated with tobacco smoking and about 400,000 were associated with high blood pressure. These two risk factors therefore each accounted for about 1 in 5 deaths in US adults. Overweight–obesity and physical inactivity were each responsible for nearly 1 in 10 deaths. Among the dietary factors examined, high dietary salt intake had the largest effect, being responsible for 4% of deaths in adults. Finally, while alcohol use prevented 26,000 deaths from ischemic heart disease, ischemic stroke, and diabetes, the researchers estimate that it caused 90,000 deaths from other types of cardiovascular diseases, other medical conditions, and road traffic accidents and violence.
What Do These Findings Mean?
These findings indicate that smoking and high blood pressure are responsible for the largest number of preventable deaths in the US, but that several other modifiable risk factors also cause many deaths. Although the accuracy of some of the estimates obtained in this study will be affected by the quality of the data used, these findings suggest that targeting a handful of risk factors could greatly reduce premature mortality in the US. The findings might also apply to other countries, although the risk factors responsible for most preventable deaths may vary between countries. Importantly, effective individual-level and population-wide interventions are already available to reduce people's exposure to the two risk factors responsible for most preventable deaths in the US. The researchers also suggest that combinations of regulation, pricing, and education have the potential to reduce the exposure of US residents to other risk factors that are likely to shorten their lives.
Additional Information
Please access these Web sites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.1000058.
The MedlinePlus encyclopedia contains a page on healthy living (in English and Spanish)
The US Centers for Disease Control and Prevention provides information on all aspects of healthy living
Healthy People 2010 is a national framework designed to improve the health of people living in the US. The Healthy People 2020 Framework is due to be launched in January 2010
The World Health Report 2002Reducing Risks, Promoting Healthy Life provides a global analysis of how healthy life expectancy could be increased
The National Health and Nutrition Examination Survey (NHANES) is “a program of studies designed to assess the health and nutritional status of adults and children in the United States”
The US Centers for Disease Control and Prevention's site Smoking and Tobacco Use offers a large number of informational and data resources on this important risk factor
The American Heart Association and American Cancer Society provide a rich resource for patients and caregivers on many important risk factors including diet, sodium intake, and smoking
doi:10.1371/journal.pmed.1000058
PMCID: PMC2667673  PMID: 19399161
14.  Long-Term Risk of Incident Type 2 Diabetes and Measures of Overall and Regional Obesity: The EPIC-InterAct Case-Cohort Study 
PLoS Medicine  2012;9(6):e1001230.
A collaborative re-analysis of data from the InterAct case-control study conducted by Claudia Langenberg and colleagues has established that waist circumference is associated with risk of type 2 diabetes, independently of body mass index.
Background
Waist circumference (WC) is a simple and reliable measure of fat distribution that may add to the prediction of type 2 diabetes (T2D), but previous studies have been too small to reliably quantify the relative and absolute risk of future diabetes by WC at different levels of body mass index (BMI).
Methods and Findings
The prospective InterAct case-cohort study was conducted in 26 centres in eight European countries and consists of 12,403 incident T2D cases and a stratified subcohort of 16,154 individuals from a total cohort of 340,234 participants with 3.99 million person-years of follow-up. We used Prentice-weighted Cox regression and random effects meta-analysis methods to estimate hazard ratios for T2D. Kaplan-Meier estimates of the cumulative incidence of T2D were calculated. BMI and WC were each independently associated with T2D, with WC being a stronger risk factor in women than in men. Risk increased across groups defined by BMI and WC; compared to low normal weight individuals (BMI 18.5–22.4 kg/m2) with a low WC (<94/80 cm in men/women), the hazard ratio of T2D was 22.0 (95% confidence interval 14.3; 33.8) in men and 31.8 (25.2; 40.2) in women with grade 2 obesity (BMI≥35 kg/m2) and a high WC (>102/88 cm). Among the large group of overweight individuals, WC measurement was highly informative and facilitated the identification of a subgroup of overweight people with high WC whose 10-y T2D cumulative incidence (men, 70 per 1,000 person-years; women, 44 per 1,000 person-years) was comparable to that of the obese group (50–103 per 1,000 person-years in men and 28–74 per 1,000 person-years in women).
Conclusions
WC is independently and strongly associated with T2D, particularly in women, and should be more widely measured for risk stratification. If targeted measurement is necessary for reasons of resource scarcity, measuring WC in overweight individuals may be an effective strategy, since it identifies a high-risk subgroup of individuals who could benefit from individualised preventive action.
Please see later in the article for the Editors' Summary
Editors' Summary
Background
Worldwide, more than 350 million people have diabetes, and this number is increasing rapidly. Diabetes is characterized by dangerous levels of glucose (sugar) in the blood. Blood sugar levels are usually controlled by insulin, a hormone that the pancreas releases after meals (digestion of food produces glucose). In people with type 2 diabetes (the commonest form of diabetes), blood sugar control fails because the fat and muscle cells that normally respond to insulin by removing sugar from the blood become insulin resistant. Type 2 diabetes can be controlled with diet and exercise, and with drugs that help the pancreas make more insulin or that make cells more sensitive to insulin. The long-term complications of diabetes, which include an increased risk of heart disease and stroke, reduce the life expectancy of people with diabetes by about 10 years compared to people without diabetes.
Why Was This Study Done?
A high body mass index (BMI, a measure of body fat calculated by dividing a person's weight in kilograms by their height in meters squared) is a strong predictor of type 2 diabetes. Although the risk of diabetes is greatest in obese people (who have a BMI of greater than 30 kg/m2), many of the people who develop diabetes are overweight—they have a BMI of 25–30 kg/m2. Healthy eating and exercise reduce the incidence of diabetes in high-risk individuals, but it is difficult and expensive to provide all overweight and obese people with individual lifestyle advice. Ideally, a way is needed to distinguish between people with high and low risk of developing diabetes at different levels of BMI. Waist circumference is a measure of fat distribution that has the potential to quantify diabetes risk among people with different BMIs because it estimates the amount of fat around the abdominal organs, which also predicts diabetes development. In this case-cohort study, the researchers use data from the InterAct study (which is investigating how genetics and lifestyle interact to affect diabetes risk) to estimate the long-term risk of type 2 diabetes associated with BMI and waist circumference. A case-cohort study measures exposure to potential risk factors in a group (cohort) of people and compares the occurrence of these risk factors in people who later develop the disease and in a randomly chosen subcohort.
What Did the Researchers Do and Find?
The researchers estimated the association of BMI and waist circumference with type 2 diabetes from baseline measurements of the weight, height, and waist circumference of 12,403 people who subsequently developed type 2 diabetes and a subcohort of 16,154 participants enrolled in the European Prospective Investigation into Cancer and Nutrition (EPIC). Both risk factors were independently associated with type 2 diabetes risk, but waist circumference was a stronger risk factor in women than in men. Obese men (BMI greater than 35 kg/m2) with a high waist circumference (greater than 102 cm) were 22 times more likely to develop diabetes than men with a low normal weight (BMI 18.5–22.4 kg/m2) and a low waist circumference (less than 94 cm); obese women with a waist circumference of more than 88 cm were 31.8 times more likely to develop type 2 diabetes than women with a low normal weight and waist circumference (less than 80 cm). Importantly, among overweight people, waist circumference measurements identified a subgroup of overweight people (those with a high waist circumference) whose 10-year cumulative incidence of type 2 diabetes was similar to that of obese people.
What Do These Findings Mean?
These findings indicate that, among people of European descent, waist circumference is independently and strongly associated with type 2 diabetes, particularly among women. Additional studies are needed to confirm this association in other ethnic groups. Targeted measurement of waist circumference in overweight individuals (who now account for a third of the US and UK adult population) could be an effective strategy for the prevention of diabetes because it would allow the identification of a high-risk subgroup of people who might benefit from individualized lifestyle advice.
Additional Information
Please access these web sites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.1001230.
The US National Diabetes Information Clearinghouse provides information about diabetes for patients, health care professionals, and the general public, including detailed information on diabetes prevention (in English and Spanish)
The US Centers for Disease Control and Prevention provides information on all aspects of overweight and obesity (including some information in Spanish)
The UK National Health Service Choices website provides information for patients and carers about type 2 diabetes, about the prevention of type 2 diabetes, and about obesity; it also includes peoples stories about diabetes and about obesity
The charity Diabetes UK also provides detailed information for patients and carers, including information on healthy lifestyles for people with diabetes, and has a further selection of stories from people with diabetes; the charity Healthtalkonline has interviews with people about their experiences of diabetes
More information on the InterAct study is available
MedlinePlus provides links to further resources and advice about diabetes and diabetes prevention and about obesity (in English and Spanish)
doi:10.1371/journal.pmed.1001230
PMCID: PMC3367997  PMID: 22679397
15.  Genetic Markers of Adult Obesity Risk Are Associated with Greater Early Infancy Weight Gain and Growth 
PLoS Medicine  2010;7(5):e1000284.
Ken Ong and colleagues genotyped children from the ALSPAC birth cohort and showed an association between greater early infancy gains in weight and length and genetic markers for adult obesity risk.
Background
Genome-wide studies have identified several common genetic variants that are robustly associated with adult obesity risk. Exploration of these genotype associations in children may provide insights into the timing of weight changes leading to adult obesity.
Methods and Findings
Children from the Avon Longitudinal Study of Parents and Children (ALSPAC) birth cohort were genotyped for ten genetic variants previously associated with adult BMI. Eight variants that showed individual associations with childhood BMI (in/near: FTO, MC4R, TMEM18, GNPDA2, KCTD15, NEGR1, BDNF, and ETV5) were used to derive an “obesity-risk-allele score” comprising the total number of risk alleles (range: 2–15 alleles) in each child with complete genotype data (n = 7,146). Repeated measurements of weight, length/height, and body mass index from birth to age 11 years were expressed as standard deviation scores (SDS). Early infancy was defined as birth to age 6 weeks, and early infancy failure to thrive was defined as weight gain between below the 5th centile, adjusted for birth weight. The obesity-risk-allele score showed little association with birth weight (regression coefficient: 0.01 SDS per allele; 95% CI 0.00–0.02), but had an apparently much larger positive effect on early infancy weight gain (0.119 SDS/allele/year; 0.023–0.216) than on subsequent childhood weight gain (0.004 SDS/allele/year; 0.004–0.005). The obesity-risk-allele score was also positively associated with early infancy length gain (0.158 SDS/allele/year; 0.032–0.284) and with reduced risk of early infancy failure to thrive (odds ratio  = 0.92 per allele; 0.86–0.98; p = 0.009).
Conclusions
The use of robust genetic markers identified greater early infancy gains in weight and length as being on the pathway to adult obesity risk in a contemporary birth cohort.
Please see later in the article for the Editors' Summary
Editors' Summary
Background
The proportion of overweight and obese children is increasing across the globe. In the US, the Surgeon General estimates that, compared with 1980, twice as many children and three times the number of adolescents are now overweight. Worldwide, 22 million children under five years old are considered by the World Health Organization to be overweight.
Being overweight or obese in childhood is associated with poor physical and mental health. In addition, childhood obesity is considered a major risk factor for adult obesity, which is itself a major risk factor for cancer, heart disease, diabetes, osteoarthritis, and other chronic conditions.
The most commonly used measure of whether an adult is a healthy weight is body mass index (BMI), defined as weight in kilograms/(height in metres)2. However, adult categories of obese (>30) and overweight (>25) BMI are not directly applicable to children, whose BMI naturally varies as they grow. BMI can be used to screen children for being overweight and or obese but a diagnosis requires further information.
Why Was This Study Done?
As the numbers of obese and overweight children increase, a corresponding rise in future numbers of overweight and obese adults is also expected. This in turn is expected to lead to an increasing incidence of poor health. As a result, there is great interest among health professionals in possible pathways between childhood and adult obesity. It has been proposed that certain periods in childhood may be critical for the development of obesity.
In the last few years, ten genetic variants have been found to be more common in overweight or obese adults. Eight of these have also been linked to childhood BMI and/or obesity. The authors wanted to identify the timing of childhood weight changes that may be associated with adult obesity. Knowledge of obesity risk genetic variants gave them an opportunity to do so now, without following a set of children to adulthood.
What Did the Researchers Do and Find?
The authors analysed data gathered from a subset of 7,146 singleton white European children enrolled in the Avon Longitudinal Study of Parents and Children (ALSPAC) study, which is investigating associations between genetics, lifestyle, and health outcomes for a group of children in Bristol whose due date of birth fell between April 1991 and December 1992. They used knowledge of the children's genetic makeup to find associations between an obesity risk allele score—a measure of how many of the obesity risk genetic variants a child possessed—and the children's weight, height, BMI, levels of body fat (at nine years old), and rate of weight gain, up to age 11 years.
They found that, at birth, children with a higher obesity risk allele score were not any heavier, but in the immediate postnatal period they were less likely to be in the bottom 5% of the population for weight gain (adjusted for birthweight), often termed “failure to thrive.” At six weeks of age, children with a higher obesity risk allele score tended to be longer and heavier, even allowing for weight at birth.
After six weeks of age, the obesity risk allele score was not associated with any further increase in length/height, but it was associated with a more rapid weight gain between birth and age 11 years. BMI is derived from height and weight measurements, and the association between the obesity risk allele score and BMI was weak between birth and age three-and-a-half years, but after that age the association with BMI increased rapidly. By age nine, children with a higher obesity risk allele score tended to be heavier and taller, with more fat on their bodies.
What Do These Findings Mean?
The combined obesity allele risk score is associated with higher rates of weight gain and adult obesity, and so the authors conclude that weight gain and growth even in the first few weeks after birth may be the beginning of a pathway of greater adult obesity risk.
A study that tracks a population over time can find associations but it cannot show cause and effect. In addition, only a relatively small proportion (1.7%) of the variation in BMI at nine years of age is explained by the obesity risk allele score.
The authors' method of finding associations between childhood events and adult outcomes via genetic markers of risk of disease as an adult has a significant advantage: the authors did not have to follow the children themselves to adulthood, so their findings are more likely to be relevant to current populations. Despite this, this research does not yield advice for parents how to reduce their children's obesity risk. It does suggest that “failure to thrive” in the first six weeks of life is not simply due to a lack of provision of food by the baby's caregiver but that genetic factors also contribute to early weight gain and growth.
The study looked at the combined obesity risk allele score and the authors did not attempt to identify which individual alleles have greater or weaker associations with weight gain and overweight or obesity. This would require further research based on far larger numbers of babies and children. The findings may also not be relevant to children in other types of setting because of the effects of different nutrition and lifestyles.
Additional Information
Please access these Web sites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.1000284.
Further information is available on the ALSPAC study
The UK National Health Service and other partners provide guidance on establishing a healthy lifestyle for children and families in their Change4Life programme
The International Obesity Taskforce is a global network of expertise and the advocacy arm of the International Association for the Study of Obesity. It works with the World Health Organization, other NGOs, and stakeholders and provides information on overweight and obesity
The Centers for Disease Control and Prevention (CDC) in the US provide guidance and tips on maintaining a healthy weight, including BMI calculators in both metric and Imperial measurements for both adults and children. They also provide BMI growth charts for boys and girls showing how healthy ranges vary for each sex at with age
The Royal College of Paediatrics and Child Health provides growth charts for weight and length/height from birth to age 4 years that are based on WHO 2006 growth standards and have been adapted for use in the UK
The CDC Web site provides information on overweight and obesity in adults and children, including definitions, causes, and data
The CDC also provide information on the role of genes in causing obesity.
The World Health Organization publishes a fact sheet on obesity, overweight and weight management, including links to childhood overweight and obesity
Wikipedia includes an article on childhood obesity (note that Wikipedia is a free online encyclopedia that anyone can edit; available in several languages)
doi:10.1371/journal.pmed.1000284
PMCID: PMC2876048  PMID: 20520848
16.  Lifestyle risk factors and residual life expectancy at age 40: a German cohort study 
BMC Medicine  2014;12:59.
Background
Cigarette smoking, adiposity, unhealthy diet, heavy alcohol drinking and physical inactivity together are associated with about half of premature deaths in Western populations. The aim of this study was to estimate their individual and combined impacts on residual life expectancy (RLE).
Methods
Lifestyle and mortality data from the EPIC-Heidelberg cohort, comprising 22,469 German adults ≥40 years and free of diabetes, cardiovascular disease and cancer at recruitment (1994–1998), were analyzed with multivariable Gompertz proportional hazards models to predict lifetime survival probabilities given specific baseline status of lifestyle risk factors. The life table method was then used to estimate the RLEs.
Results
For 40-year-old adults, the most significant loss of RLE was associated with smoking (9.4 [95% confidence interval: 8.3, 10.6] years for male and 7.3 [6.0, 8.9] years for female heavy smokers [>10 cigarettes/day]; 5.3 [3.6, 7.1] years for men and 5.0 [3.2, 6.6] years for women smoking ≤10 cigarettes/day). Other lifestyle risk factors associated with major losses of RLE were low body mass index (BMI <22.5 kg/m2, 3.5 [1.8, 5.1] years for men; 2.1 [0.5, 3.6] years for women), obesity (BMI ≥30, 3.1 [1.9, 4.4] years for men; 3.2 [1.8, 5.1] years for women), heavy alcohol drinking (>4 drinks/day, 3.1 [1.9, 4.0] years for men), and high processed/red meat consumption (≥120 g/day, 2.4 [1.0, 3.9] years for women). The obesity-associated loss of RLE was stronger in male never smokers, while the loss of RLE associated with low BMI was stronger in current smokers. The loss of RLE associated with low leisure time physical activity was moderate for women (1.1 [0.05, 2.1] years) and negligible for men (0.4 [−0.3, 1.2] years). The combined loss of RLE for heavy smoking, obesity, heavy alcohol drinking and high processed/red meat consumption, versus never smoking, optimal BMI (22.5 to 24.9), no/light alcohol drinking and low processed/red meat consumption, was 17.0 years for men and 13.9 years for women.
Conclusions
Promoting healthy lifestyles, particularly no cigarette smoking and maintaining healthy body weight, should be the core component of public health approaches to reducing premature deaths in Germany and similar affluent societies.
doi:10.1186/1741-7015-12-59
PMCID: PMC4022368  PMID: 24708705
Lifestyle risk factors; Residual life expectancy; Cohort study
17.  Metabolic Signatures of Adiposity in Young Adults: Mendelian Randomization Analysis and Effects of Weight Change 
PLoS Medicine  2014;11(12):e1001765.
In this study, Wurtz and colleagues investigated to what extent elevated body mass index (BMI) within the normal weight range has causal influences on the detailed systemic metabolite profile in early adulthood using Mendelian randomization analysis.
Please see later in the article for the Editors' Summary
Background
Increased adiposity is linked with higher risk for cardiometabolic diseases. We aimed to determine to what extent elevated body mass index (BMI) within the normal weight range has causal effects on the detailed systemic metabolite profile in early adulthood.
Methods and Findings
We used Mendelian randomization to estimate causal effects of BMI on 82 metabolic measures in 12,664 adolescents and young adults from four population-based cohorts in Finland (mean age 26 y, range 16–39 y; 51% women; mean ± standard deviation BMI 24±4 kg/m2). Circulating metabolites were quantified by high-throughput nuclear magnetic resonance metabolomics and biochemical assays. In cross-sectional analyses, elevated BMI was adversely associated with cardiometabolic risk markers throughout the systemic metabolite profile, including lipoprotein subclasses, fatty acid composition, amino acids, inflammatory markers, and various hormones (p<0.0005 for 68 measures). Metabolite associations with BMI were generally stronger for men than for women (median 136%, interquartile range 125%–183%). A gene score for predisposition to elevated BMI, composed of 32 established genetic correlates, was used as the instrument to assess causality. Causal effects of elevated BMI closely matched observational estimates (correspondence 87%±3%; R2 = 0.89), suggesting causative influences of adiposity on the levels of numerous metabolites (p<0.0005 for 24 measures), including lipoprotein lipid subclasses and particle size, branched-chain and aromatic amino acids, and inflammation-related glycoprotein acetyls. Causal analyses of certain metabolites and potential sex differences warrant stronger statistical power. Metabolite changes associated with change in BMI during 6 y of follow-up were examined for 1,488 individuals. Change in BMI was accompanied by widespread metabolite changes, which had an association pattern similar to that of the cross-sectional observations, yet with greater metabolic effects (correspondence 160%±2%; R2 = 0.92).
Conclusions
Mendelian randomization indicates causal adverse effects of increased adiposity with multiple cardiometabolic risk markers across the metabolite profile in adolescents and young adults within the non-obese weight range. Consistent with the causal influences of adiposity, weight changes were paralleled by extensive metabolic changes, suggesting a broadly modifiable systemic metabolite profile in early adulthood.
Please see later in the article for the Editors' Summary
Editors' Summary
Background
Adiposity—having excessive body fat—is a growing global threat to public health. Body mass index (BMI, calculated by dividing a person's weight in kilograms by their height in meters squared) is a coarse indicator of excess body weight, but the measure is useful in large population studies. Compared to people with a lean body weight (a BMI of 18.5–24.9 kg/m2), individuals with higher BMI have an elevated risk of developing life-shortening cardiometabolic diseases—cardiovascular diseases that affect the heart and/or the blood vessels (for example, heart failure and stroke) and metabolic diseases that affect the cellular chemical reactions that sustain life (for example, diabetes). People become unhealthily fat by consuming food and drink that contains more energy (calories) than they need for their daily activities. So adiposity can be prevented and reversed by eating less and exercising more.
Why Was This Study Done?
Epidemiological studies, which record the patterns of risk factors and disease in populations, suggest that the illness and death associated with excess body weight is partly attributable to abnormalities in how individuals with high adiposity metabolize carbohydrates and fats, leading to higher blood sugar and cholesterol levels. Further, adiposity is also associated with many other deviations in the metabolic profile than these commonly measured risk factors. However, epidemiological studies cannot prove that adiposity causes specific changes in a person's systemic (overall) metabolic profile because individuals with high BMI may share other characteristics (confounding factors) that are the actual causes of both adiposity and metabolic abnormalities. Moreover, having a change in some aspect of metabolism could also lead to adiposity, rather than vice versa (reverse causation). Importantly, if there is a causal effect of adiposity on cardiometabolic risk factor levels, it might be possible to prevent the progression towards cardiometabolic diseases by weight loss. Here, the researchers use “Mendelian randomization” to examine whether increased BMI within the normal and overweight range is causally influencing the metabolic risk factors from many biological pathways during early adulthood. Because gene variants are inherited randomly, they are not prone to confounding and are free from reverse causation. Several gene variants are known to lead to modestly increased BMI. Thus, an investigation of the associations between these gene variants and risk factors across the systemic metabolite profile in a population of healthy individuals can indicate whether higher BMI is causally related to known and novel metabolic risk factors and higher cardiometabolic disease risk.
What Did the Researchers Do and Find?
The researchers measured the BMI of 12,664 adolescents and young adults (average BMI 24.7 kg/m2) living in Finland and the blood levels of 82 metabolites in these young individuals at a single time point. Statistical analysis of these data indicated that elevated BMI was adversely associated with numerous cardiometabolic risk factors. For example, elevated BMI was associated with raised levels of low-density lipoprotein, “bad” cholesterol that increases cardiovascular disease risk. Next, the researchers used a gene score for predisposition to increased BMI, composed of 32 gene variants correlated with increased BMI, as an “instrumental variable” to assess whether adiposity causes metabolite abnormalities. The effects on the systemic metabolite profile of a 1-kg/m2 increment in BMI due to genetic predisposition closely matched the effects of an observed 1-kg/m2 increment in adulthood BMI on the metabolic profile. That is, higher levels of adiposity had causal effects on the levels of numerous blood-based metabolic risk factors, including higher levels of low-density lipoprotein cholesterol and triglyceride-carrying lipoproteins, protein markers of chronic inflammation and adverse liver function, impaired insulin sensitivity, and elevated concentrations of several amino acids that have recently been linked with the risk for developing diabetes. Elevated BMI also causally led to lower levels of certain high-density lipoprotein lipids in the blood, a marker for the risk of future cardiovascular disease. Finally, an examination of the metabolic changes associated with changes in BMI in 1,488 young adults after a period of six years showed that those metabolic measures that were most strongly associated with BMI at a single time point likewise displayed the highest responsiveness to weight change over time.
What Do These Findings Mean?
These findings suggest that increased adiposity has causal adverse effects on multiple cardiometabolic risk markers in non-obese young adults beyond the effects on cholesterol and blood sugar. Like all Mendelian randomization studies, the reliability of the causal association reported here depends on several assumptions made by the researchers. Nevertheless, these findings suggest that increased adiposity has causal adverse effects on multiple cardiometabolic risk markers in non-obese young adults. Importantly, the results of both the causal effect analyses and the longitudinal study suggest that there is no threshold below which a BMI increase does not adversely affect the metabolic profile, and that a systemic metabolic profile linked with high cardiometabolic disease risk that becomes established during early adulthood can be reversed. Overall, these findings therefore highlight the importance of weight reduction as a key target for metabolic risk factor control among young adults.
Additional Information
Please access these websites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.1001765.
The Computational Medicine Research Team of the University of Oulu has a webpage that provides further information on metabolite profiling by high-throughput NMR metabolomics
The World Health Organization provides information on obesity (in several languages)
The Global Burden of Disease Study website provides the latest details about global obesity trends
The UK National Health Service Choices website provides information about obesity, cardiovascular disease, and type 2 diabetes (including some personal stories)
The American Heart Association provides information on all aspects of cardiovascular disease and diabetes and on keeping healthy; its website includes personal stories about heart attacks, stroke, and diabetes
The US Centers for Disease Control and Prevention has information on all aspects of overweight and obesity and information about heart disease, stroke, and diabetes
MedlinePlus provides links to other sources of information on heart disease, vascular disease, and obesity (in English and Spanish)
Wikipedia has a page on Mendelian randomization (note: Wikipedia is a free online encyclopedia that anyone can edit; available in several languages)
doi:10.1371/journal.pmed.1001765
PMCID: PMC4260795  PMID: 25490400
18.  Personalized Prediction of Lifetime Benefits with Statin Therapy for Asymptomatic Individuals: A Modeling Study 
PLoS Medicine  2012;9(12):e1001361.
In a modeling study conducted by Myriam Hunink and colleagues, a population-based cohort from Rotterdam is used to predict the possible lifetime benefits of statin therapy, on a personalized basis.
Background
Physicians need to inform asymptomatic individuals about personalized outcomes of statin therapy for primary prevention of cardiovascular disease (CVD). However, current prediction models focus on short-term outcomes and ignore the competing risk of death due to other causes. We aimed to predict the potential lifetime benefits with statin therapy, taking into account competing risks.
Methods and Findings
A microsimulation model based on 5-y follow-up data from the Rotterdam Study, a population-based cohort of individuals aged 55 y and older living in the Ommoord district of Rotterdam, the Netherlands, was used to estimate lifetime outcomes with and without statin therapy. The model was validated in-sample using 10-y follow-up data. We used baseline variables and model output to construct (1) a web-based calculator for gains in total and CVD-free life expectancy and (2) color charts for comparing these gains to the Systematic Coronary Risk Evaluation (SCORE) charts. In 2,428 participants (mean age 67.7 y, 35.5% men), statin therapy increased total life expectancy by 0.3 y (SD 0.2) and CVD-free life expectancy by 0.7 y (SD 0.4). Age, sex, smoking, blood pressure, hypertension, lipids, diabetes, glucose, body mass index, waist-to-hip ratio, and creatinine were included in the calculator. Gains in total and CVD-free life expectancy increased with blood pressure, unfavorable lipid levels, and body mass index after multivariable adjustment. Gains decreased considerably with advancing age, while SCORE 10-y CVD mortality risk increased with age. Twenty-five percent of participants with a low SCORE risk achieved equal or larger gains in CVD-free life expectancy than the median gain in participants with a high SCORE risk.
Conclusions
We developed tools to predict personalized increases in total and CVD-free life expectancy with statin therapy. The predicted gains we found are small. If the underlying model is validated in an independent cohort, the tools may be useful in discussing with patients their individual outcomes with statin therapy.
Please see later in the article for the Editors' Summary
Editors' Summary
Background
Cardiovascular disease (CVD) affects the heart and/or the blood vessels and is a major cause of illness and death worldwide. In the US, for example, coronary heart disease—a CVD in which narrowing of the heart's blood vessels by fatty deposits slows the blood supply to the heart and may eventually cause a heart attack—is the leading cause of death, and stroke—a CVD in which the brain's blood supply is interrupted—is the fourth leading cause of death. Established risk factors for CVD include smoking, high blood pressure, obesity, and high blood levels of a fat called low-density lipoprotein (“bad cholesterol”). Because many of these risk factors can be modified by lifestyle changes and by drugs, CVD can be prevented. Thus, physicians can assess a healthy individual's risk of developing CVD using a CVD prediction model (equations that take into account the CVD risk factors to which the individual is exposed) and can then recommend lifestyle changes and medications to reduce that individual's CVD risk.
Why Was This Study Done?
Current guidelines recommend that asymptomatic (healthy) individuals whose likely CVD risk is high should be encouraged to take statins—cholesterol-lowering drugs—as a preventative measure. Statins help to prevent CVD in healthy people with a high predicted risk of CVD, but, like all medicines, they have some unwanted side effects, so it is important that physicians can communicate both the benefits and drawbacks of statins to their patients in a way that allows them to make an informed decision about taking these drugs. Telling a patient that statins will reduce his or her short-term risk of CVD is not always helpful—patients really need to know the potential lifetime benefits of statin therapy. That is, they need to know how much longer they might live if they take statins. Here, the researchers use a mathematical model to predict the personalized lifetime benefits (increased total and CVD-free life expectancy) of statin therapy for individuals without a history of CVD.
What Did the Researchers Do and Find?
The researchers used the Rotterdam Ischemic Heart Disease & Stroke Computer Simulation (RISC) model, which simulates the life courses of individuals through six health states, from well through to CVD or non-CVD death, to estimate lifetime outcomes with and without statin therapy in a population of healthy elderly individuals. They then used these outcomes and information on baseline risk factors to develop a web-based calculator suitable for personalized prediction of the lifetime benefits of statins in routine clinical practice. The model estimated that statin therapy increases average life expectancy in the study population by 0.3 years and average CVD-free life expectancy by 0.7 years. The gains in total and CVD-free life expectancy associated with statin therapy increased with blood pressure, unfavorable cholesterol levels, and body mass index (an indicator of body fat) but decreased with age. Notably, the web-based calculator predicted that some individuals with a low ten-year CVD risk might achieve a similar or larger gain in CVD-free life expectancy with statin therapy than some individuals with a high ten-year risk. So, for example, both a 55-year-old non-smoking woman with a ten-year CVD mortality risk of 2% (a two in a hundred chance of dying of CVD within ten years) and a 65-year-old male smoker with a ten-year CVD mortality risk of 15% might both gain one year of CVD-free life expectancy with statin therapy.
What Do These Findings Mean?
These findings suggest that statin therapy can lead on average to small gains in total life expectancy and slightly larger gains in CVD-free life expectancy among healthy individuals, and show that life expectancy benefits can be predicted using an individual's risk factor profile. The accuracy and generalizability of these findings is limited by the assumptions included in the model (in particular, the model did not allow for the known side effects of statin therapy) and by the data fed into it—importantly, the risk prediction model needs to be validated using an independent dataset. If future research confirms the findings of this study, the researchers' web-based calculator could provide complementary information to the currently recommended ten-year CVD mortality risk assessment. Whether communication of personalized outcomes will ultimately result in better clinical outcomes remains to be seen, however, because patients may be less likely to choose statin therapy when provided with more information about its likely benefits.
Additional Information
Please access these websites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.1001361.
The web-based calculator for personalized prediction of lifetime benefits with statin therapy is available (after agreement to software license)
The American Heart Association provides information about many types of cardiovascular disease for patients, carers, and professionals, including information about drug therapy for cholesterol and a heart attack risk calculator
The UK National Health Service Choices website provides information about cardiovascular disease and about statins
Information is available from the British Heart Foundation on heart disease and keeping the heart healthy; information is also available on statins, including personal stories about deciding to take statins
The US National Heart Lung and Blood Institute provides information on a wide range of cardiovascular diseases
The European Society of Cardiology's cardiovascular disease risk assessment model (SCORE) is available
MedlinePlus provides links to many other sources of information on heart diseases, vascular diseases, stroke, and statins (in English and Spanish)
doi:10.1371/journal.pmed.1001361
PMCID: PMC3531501  PMID: 23300388
19.  Burden of Total and Cause-Specific Mortality Related to Tobacco Smoking among Adults Aged ≥45 Years in Asia: A Pooled Analysis of 21 Cohorts 
PLoS Medicine  2014;11(4):e1001631.
Wei Zheng and colleagues quantify the burden of tobacco-smoking-related deaths for adults in Asia.
Please see later in the article for the Editors' Summary
Background
Tobacco smoking is a major risk factor for many diseases. We sought to quantify the burden of tobacco-smoking-related deaths in Asia, in parts of which men's smoking prevalence is among the world's highest.
Methods and Findings
We performed pooled analyses of data from 1,049,929 participants in 21 cohorts in Asia to quantify the risks of total and cause-specific mortality associated with tobacco smoking using adjusted hazard ratios and their 95% confidence intervals. We then estimated smoking-related deaths among adults aged ≥45 y in 2004 in Bangladesh, India, mainland China, Japan, Republic of Korea, Singapore, and Taiwan—accounting for ∼71% of Asia's total population. An approximately 1.44-fold (95% CI = 1.37–1.51) and 1.48-fold (1.38–1.58) elevated risk of death from any cause was found in male and female ever-smokers, respectively. In 2004, active tobacco smoking accounted for approximately 15.8% (95% CI = 14.3%–17.2%) and 3.3% (2.6%–4.0%) of deaths, respectively, in men and women aged ≥45 y in the seven countries/regions combined, with a total number of estimated deaths of ∼1,575,500 (95% CI = 1,398,000–1,744,700). Among men, approximately 11.4%, 30.5%, and 19.8% of deaths due to cardiovascular diseases, cancer, and respiratory diseases, respectively, were attributable to tobacco smoking. Corresponding proportions for East Asian women were 3.7%, 4.6%, and 1.7%, respectively. The strongest association with tobacco smoking was found for lung cancer: a 3- to 4-fold elevated risk, accounting for 60.5% and 16.7% of lung cancer deaths, respectively, in Asian men and East Asian women aged ≥45 y.
Conclusions
Tobacco smoking is associated with a substantially elevated risk of mortality, accounting for approximately 2 million deaths in adults aged ≥45 y throughout Asia in 2004. It is likely that smoking-related deaths in Asia will continue to rise over the next few decades if no effective smoking control programs are implemented.
Please see later in the article for the Editors' Summary
Editors' Summary
Background
Every year, more than 5 million smokers die from tobacco-related diseases. Tobacco smoking is a major risk factor for cardiovascular disease (conditions that affect the heart and the circulation), respiratory disease (conditions that affect breathing), lung cancer, and several other types of cancer. All told, tobacco smoking kills up to half its users. The ongoing global “epidemic” of tobacco smoking and tobacco-related diseases initially affected people living in the US and other Western countries, where the prevalence of smoking (the proportion of the population that smokes) in men began to rise in the early 1900s, peaking in the 1960s. A similar epidemic occurred in women about 40 years later. Smoking-related deaths began to increase in the second half of the 20th century, and by the 1990s, tobacco smoking accounted for a third of all deaths and about half of cancer deaths among men in the US and other Western countries. More recently, increased awareness of the risks of smoking and the introduction of various tobacco control measures has led to a steady decline in tobacco use and in smoking-related diseases in many developed countries.
Why Was This Study Done?
Unfortunately, less well-developed tobacco control programs, inadequate public awareness of smoking risks, and tobacco company marketing have recently led to sharp increases in the prevalence of smoking in many low- and middle-income countries, particularly in Asia. More than 50% of men in many Asian countries are now smokers, about twice the prevalence in many Western countries, and more women in some Asian countries are smoking than previously. More than half of the world's billion smokers now live in Asia. However, little is known about the burden of tobacco-related mortality (deaths) in this region. In this study, the researchers quantify the risk of total and cause-specific mortality associated with tobacco use among adults aged 45 years or older by undertaking a pooled statistical analysis of data collected from 21 Asian cohorts (groups) about their smoking history and health.
What Did the Researchers Do and Find?
For their study, the researchers used data from more than 1 million participants enrolled in studies undertaken in Bangladesh, India, mainland China, Japan, the Republic of Korea, Singapore, and Taiwan (which together account for 71% of Asia's total population). Smoking prevalences among male and female participants were 65.1% and 7.1%, respectively. Compared with never-smokers, ever-smokers had a higher risk of death from any cause in pooled analyses of all the cohorts (adjusted hazard ratios [HRs] of 1.44 and 1.48 for men and women, respectively; an adjusted HR indicates how often an event occurs in one group compared to another group after adjustment for other characteristics that affect an individual's risk of the event). Compared with never smoking, ever smoking was associated with a higher risk of death due to cardiovascular disease, cancer (particularly lung cancer), and respiratory disease among Asian men and among East Asian women. Moreover, the researchers estimate that, in the countries included in this study, tobacco smoking accounted for 15.8% of all deaths among men and 3.3% of deaths among women in 2004—a total of about 1.5 million deaths, which scales up to 2 million deaths for the population of the whole of Asia. Notably, in 2004, tobacco smoking accounted for 60.5% of lung-cancer deaths among Asian men and 16.7% of lung-cancer deaths among East Asian women.
What Do These Findings Mean?
These findings provide strong evidence that tobacco smoking is associated with a substantially raised risk of death among adults aged 45 years or older throughout Asia. The association between smoking and mortality risk in Asia reported here is weaker than that previously reported for Western countries, possibly because widespread tobacco smoking started several decades later in most Asian countries than in Europe and North America and the deleterious effects of smoking take some years to become evident. The researchers note that certain limitations of their analysis are likely to affect the accuracy of its findings. For example, because no data were available to estimate the impact of secondhand smoke, the estimate of deaths attributable to smoking is likely to be an underestimate. However, the finding that nearly 45% of the global deaths from active tobacco smoking occur in Asia highlights the urgent need to implement comprehensive tobacco control programs in Asia to reduce the burden of tobacco-related disease.
Additional Information
Please access these websites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.1001631.
The World Health Organization provides information about the dangers of tobacco (in several languages) and about the WHO Framework Convention on Tobacco Control, an international instrument for tobacco control that came into force in February 2005 and requires parties to implement a set of core tobacco control provisions including legislation to ban tobacco advertising and to increase tobacco taxes; its 2013 report on the global tobacco epidemic is available
The US Centers for Disease Control and Prevention provides detailed information about all aspects of smoking and tobacco use
The UK National Health Services Choices website provides information about the health risks associated with smoking
MedlinePlus has links to further information about the dangers of smoking (in English and Spanish)
SmokeFree, a website provided by the UK National Health Service, offers advice on quitting smoking and includes personal stories from people who have stopped smoking
Smokefree.gov, from the US National Cancer Institute, offers online tools and resources to help people quit smoking
doi:10.1371/journal.pmed.1001631
PMCID: PMC3995657  PMID: 24756146
20.  Health-Adjusted Life Expectancy among Canadian Adults with and without Hypertension 
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.
doi:10.4061/2011/612968
PMCID: PMC3123912  PMID: 21738858
21.  The Reversal of Fortunes: Trends in County Mortality and Cross-County Mortality Disparities in the United States  
PLoS Medicine  2008;5(4):e66.
Background
Counties are the smallest unit for which mortality data are routinely available, allowing consistent and comparable long-term analysis of trends in health disparities. Average life expectancy has steadily increased in the United States but there is limited information on long-term mortality trends in the US counties This study aimed to investigate trends in county mortality and cross-county mortality disparities, including the contributions of specific diseases to county level mortality trends.
Methods and Findings
We used mortality statistics (from the National Center for Health Statistics [NCHS]) and population (from the US Census) to estimate sex-specific life expectancy for US counties for every year between 1961 and 1999. Data for analyses in subsequent years were not provided to us by the NCHS. We calculated different metrics of cross-county mortality disparity, and also grouped counties on the basis of whether their mortality changed favorably or unfavorably relative to the national average. We estimated the probability of death from specific diseases for counties with above- or below-average mortality performance. We simulated the effect of cross-county migration on each county's life expectancy using a time-based simulation model. Between 1961 and 1999, the standard deviation (SD) of life expectancy across US counties was at its lowest in 1983, at 1.9 and 1.4 y for men and women, respectively. Cross-county life expectancy SD increased to 2.3 and 1.7 y in 1999. Between 1961 and 1983 no counties had a statistically significant increase in mortality; the major cause of mortality decline for both sexes was reduction in cardiovascular mortality. From 1983 to 1999, life expectancy declined significantly in 11 counties for men (by 1.3 y) and in 180 counties for women (by 1.3 y); another 48 (men) and 783 (women) counties had nonsignificant life expectancy decline. Life expectancy decline in both sexes was caused by increased mortality from lung cancer, chronic obstructive pulmonary disease (COPD), diabetes, and a range of other noncommunicable diseases, which were no longer compensated for by the decline in cardiovascular mortality. Higher HIV/AIDS and homicide deaths also contributed substantially to life expectancy decline for men, but not for women. Alternative specifications of the effects of migration showed that the rise in cross-county life expectancy SD was unlikely to be caused by migration.
Conclusions
There was a steady increase in mortality inequality across the US counties between 1983 and 1999, resulting from stagnation or increase in mortality among the worst-off segment of the population. Female mortality increased in a large number of counties, primarily because of chronic diseases related to smoking, overweight and obesity, and high blood pressure.
Majid Ezzati and colleagues analyze US county-level mortality data for 1961 to 1999, and find a steady increase in mortality inequality across counties between 1983 and 1999.
Editors' Summary
Background.
It has long been recognized that the number of years that distinct groups of people in the United States would be expected to live based on their current mortality patterns (“life expectancy”) varies enormously. For example, white Americans tend to live longer than black Americans, the poor tend to have shorter life expectancies than the wealthy, and women tend to outlive men. Where one lives might also be a factor that determines his or her life expectancy, because of social conditions and health programs in different parts of the country.
Why Was the Study Done?
While life expectancies have generally been rising across the United States over time, there is little information, especially over the long term, on the differences in life expectancies across different counties. The researchers therefore set out to examine whether there were different life expectancies across different US counties over the last four decades. The researchers chose to look at counties—the smallest geographic units for which data on death rates are collected in the US—because it allowed them to make comparisons between small subgroups of people that share the same administrative structure.
What Did the Researchers Do and Find?
The researchers looked at differences in death rates between all counties in US states plus the District of Columbia over four decades, from 1961 to 1999. They obtained the data on number of deaths from the National Center for Health Statistics, and they obtained data on the number of people living in each county from the US Census. The NCHS did not provide death data after 2001. They broke the death rates down by sex and by disease to assess trends over time for women and men, and for different causes of death.
Over these four decades, the researchers found that the overall US life expectancy increased from 67 to 74 years of age for men and from 74 to 80 years for women. Between 1961 and 1983 the death rate fell in both men and women, largely due to reductions in deaths from cardiovascular disease (heart disease and stroke). During this same period, 1961–1983, the differences in death rates among/across different counties fell. However, beginning in the early 1980s the differences in death rates among/across different counties began to increase. The worst-off counties no longer experienced a fall in death rates, and in a substantial number of counties, mortality actually increased, especially for women, a shift that the researchers call “the reversal of fortunes.” This stagnation in the worst-off counties was primarily caused by a slowdown or halt in the reduction of deaths from cardiovascular disease coupled with a moderate rise in a number of other diseases, such as lung cancer, chronic lung disease, and diabetes, in both men and women, and a rise in HIV/AIDS and homicide in men. The researchers' key finding, therefore, was that the differences in life expectancy across different counties initially narrowed and then widened.
What Do these Findings Mean?
The findings suggest that beginning in the early 1980s and continuing through 1999 those who were already disadvantaged did not benefit from the gains in life expectancy experienced by the advantaged, and some became even worse off. The study emphasizes how important it is to monitor health inequalities between different groups, in order to ensure that everyone—and not just the well-off—can experience gains in life expectancy. Although the “reversal of fortune” that the researchers found applied to only a minority of the population, the authors argue that their study results are troubling because an oft-stated aim of the US health system is the improvement of the health of “all people, and especially those at greater risk of health disparities” (see, for example http://www.cdc.gov/osi/goals/SIHPGPostcard.pdf).
Additional Information.
Please access these Web sites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.0050066.
A study by Nancy Krieger and colleagues, published in PLoS Medicine in February 2008, documented a similar “fall and rise” in health inequities. Krieger and colleagues reported that the difference in health between rich and poor and between different racial/ethnic groups, as measured by rates of dying young and of infant deaths, shrank in the US from 1966 to 1980 then widened from 1980 to 2002
Murray and colleagues, in a 2006 PLoS Medicine article, calculated US mortality rates according to “race-county” units and divided into the “eight Americas,” and found disparities in life expectancy across them
The US Centers for Disease Control has an Office of Minority Health and Health Disparities. The office “aims to accelerate CDC's health impact in the US population and to eliminate health disparities for vulnerable populations as defined by race/ethnicity, socioeconomic status, geography, gender, age, disability status, risk status related to sex and gender, and among other populations identified to be at-risk for health disparities”
Wikipedia has a chapter on health disparities (note that Wikipedia is a free online encyclopedia that anyone can edit; available in several languages)
In 2001 the US Agency for Healthcare Research and Quality sponsored a workshop on “strategies to reduce health disparities”
doi:10.1371/journal.pmed.0050066
PMCID: PMC2323303  PMID: 18433290
22.  Risk Prediction for Breast, Endometrial, and Ovarian Cancer in White Women Aged 50 y or Older: Derivation and Validation from Population-Based Cohort Studies 
PLoS Medicine  2013;10(7):e1001492.
Ruth Pfeiffer and colleagues describe models to calculate absolute risks for breast, endometrial, and ovarian cancers for white, non-Hispanic women over 50 years old using easily obtainable risk factors.
Please see later in the article for the Editors' Summary
Background
Breast, endometrial, and ovarian cancers share some hormonal and epidemiologic risk factors. While several models predict absolute risk of breast cancer, there are few models for ovarian cancer in the general population, and none for endometrial cancer.
Methods and Findings
Using data on white, non-Hispanic women aged 50+ y from two large population-based cohorts (the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial [PLCO] and the National Institutes of Health–AARP Diet and Health Study [NIH-AARP]), we estimated relative and attributable risks and combined them with age-specific US-population incidence and competing mortality rates. All models included parity. The breast cancer model additionally included estrogen and progestin menopausal hormone therapy (MHT) use, other MHT use, age at first live birth, menopausal status, age at menopause, family history of breast or ovarian cancer, benign breast disease/biopsies, alcohol consumption, and body mass index (BMI); the endometrial model included menopausal status, age at menopause, BMI, smoking, oral contraceptive use, MHT use, and an interaction term between BMI and MHT use; the ovarian model included oral contraceptive use, MHT use, and family history or breast or ovarian cancer. In independent validation data (Nurses' Health Study cohort) the breast and ovarian cancer models were well calibrated; expected to observed cancer ratios were 1.00 (95% confidence interval [CI]: 0.96–1.04) for breast cancer and 1.08 (95% CI: 0.97–1.19) for ovarian cancer. The number of endometrial cancers was significantly overestimated, expected/observed = 1.20 (95% CI: 1.11–1.29). The areas under the receiver operating characteristic curves (AUCs; discriminatory power) were 0.58 (95% CI: 0.57–0.59), 0.59 (95% CI: 0.56–0.63), and 0.68 (95% CI: 0.66–0.70) for the breast, ovarian, and endometrial models, respectively.
Conclusions
These models predict absolute risks for breast, endometrial, and ovarian cancers from easily obtainable risk factors and may assist in clinical decision-making. Limitations are the modest discriminatory ability of the breast and ovarian models and that these models may not generalize to women of other races.
Please see later in the article for the Editors' Summary
Editors' Summary
Background
In 2008, just three types of cancer accounted for 10% of global cancer-related deaths. That year, about 460,000 women died from breast cancer (the most frequently diagnosed cancer among women and the fifth most common cause of cancer-related death). Another 140,000 women died from ovarian cancer, and 74,000 died from endometrial (womb) cancer (the 14th and 20th most common causes of cancer-related death, respectively). Although these three cancers originate in different tissues, they nevertheless share many risk factors. For example, current age, age at menarche (first period), and parity (the number of children a woman has had) are all strongly associated with breast, ovarian, and endometrial cancer risk. Because these cancers share many hormonal and epidemiological risk factors, a woman with a high breast cancer risk is also likely to have an above-average risk of developing ovarian or endometrial cancer.
Why Was This Study Done?
Several statistical models (for example, the Breast Cancer Risk Assessment Tool) have been developed that estimate a woman's absolute risk (probability) of developing breast cancer over the next few years or over her lifetime. Absolute risk prediction models are useful in the design of cancer prevention trials and can also help women make informed decisions about cancer prevention and treatment options. For example, a woman at high risk of breast cancer might decide to take tamoxifen for breast cancer prevention, but ideally she needs to know her absolute endometrial cancer risk before doing so because tamoxifen increases the risk of this cancer. Similarly, knowledge of her ovarian cancer risk might influence a woman's decision regarding prophylactic removal of her ovaries to reduce her breast cancer risk. There are few absolute risk prediction models for ovarian cancer, and none for endometrial cancer, so here the researchers develop models to predict the risk of these cancers and of breast cancer.
What Did the Researchers Do and Find?
Absolute risk prediction models are constructed by combining estimates for risk factors from cohorts with population-based incidence rates from cancer registries. Models are validated in an independent cohort by testing their ability to identify people with the disease in an independent cohort and their ability to predict the observed numbers of incident cases. The researchers used data on white, non-Hispanic women aged 50 years or older that were collected during two large prospective US cohort studies of cancer screening and of diet and health, and US cancer incidence and mortality rates provided by the Surveillance, Epidemiology, and End Results Program to build their models. The models all included parity as a risk factor, as well as other factors. The model for endometrial cancer, for example, also included menopausal status, age at menopause, body mass index (an indicator of the amount of body fat), oral contraceptive use, menopausal hormone therapy use, and an interaction term between menopausal hormone therapy use and body mass index. Individual women's risk for endometrial cancer calculated using this model ranged from 1.22% to 17.8% over the next 20 years depending on their exposure to various risk factors. Validation of the models using data from the US Nurses' Health Study indicated that the endometrial cancer model overestimated the risk of endometrial cancer but that the breast and ovarian cancer models were well calibrated—the predicted and observed risks for these cancers in the validation cohort agreed closely. Finally, the discriminatory power of the models (a measure of how well a model separates people who have a disease from people who do not have the disease) was modest for the breast and ovarian cancer models but somewhat better for the endometrial cancer model.
What Do These Findings Mean?
These findings show that breast, ovarian, and endometrial cancer can all be predicted using information on known risk factors for these cancers that is easily obtainable. Because these models were constructed and validated using data from white, non-Hispanic women aged 50 years or older, they may not accurately predict absolute risk for these cancers for women of other races or ethnicities. Moreover, the modest discriminatory power of the breast and ovarian cancer models means they cannot be used to decide which women should be routinely screened for these cancers. Importantly, however, these well-calibrated models should provide realistic information about an individual's risk of developing breast, ovarian, or endometrial cancer that can be used in clinical decision-making and that may assist in the identification of potential participants for research studies.
Additional Information
Please access these websites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.1001492.
This study is further discussed in a PLOS Medicine Perspective by Lars Holmberg and Andrew Vickers
The US National Cancer Institute provides comprehensive information about cancer (in English and Spanish), including detailed information about breast cancer, ovarian cancer, and endometrial cancer;
Information on the Breast Cancer Risk Assessment Tool, the Surveillance, Epidemiology, and End Results Program, and on the prospective cohort study of screening and the diet and health study that provided the data used to build the models is also available on the NCI site
Cancer Research UK, a not-for-profit organization, provides information about cancer, including detailed information on breast cancer, ovarian cancer, and endometrial cancer
The UK National Health Service Choices website has information and personal stories about breast cancer, ovarian cancer, and endometrial cancer; the not-for-profit organization Healthtalkonline also provides personal stories about dealing with breast cancer and ovarian cancer
doi:10.1371/journal.pmed.1001492
PMCID: PMC3728034  PMID: 23935463
23.  Disability Transitions and Health Expectancies among Adults 45 Years and Older in Malawi: A Cohort-Based Model 
PLoS Medicine  2013;10(5):e1001435.
Collin Payne and colleagues investigated development of disabilities and years expected to live with disabilities in participants 45 years and older participating in the Malawi Longitudinal Survey of Families and Health.
Please see later in the article for the Editors' Summary
Background
Falling fertility and increasing life expectancy contribute to a growing elderly population in sub-Saharan Africa (SSA); by 2060, persons aged 45 y and older are projected to be 25% of SSA's population, up from 10% in 2010. Aging in SSA is associated with unique challenges because of poverty and inadequate social supports. However, despite its importance for understanding the consequences of population aging, the evidence about the prevalence of disabilities and functional limitations due to poor physical health among older adults in SSA continues to be very limited.
Methods and Findings
Participants came from 2006, 2008, and 2010 waves of the Malawi Longitudinal Survey of Families and Health, a study of the rural population in Malawi. We investigate how poor physical health results in functional limitations that limit the day-to-day activities of individuals in domains relevant to this subsistence-agriculture context. These disabilities were parameterized based on questions from the SF-12 questionnaire about limitations in daily living activities. We estimated age-specific patterns of functional limitations and the transitions over time between different disability states using a discrete-time hazard model. The estimated transition rates were then used to calculate the first (to our knowledge) microdata-based health expectancies calculated for SSA. The risks of experiencing functional limitations due to poor physical health are high in this population, and the onset of disabilities happens early in life. Our analyses show that 45-y-old women can expect to spend 58% (95% CI, 55%–64%) of their remaining 28 y of life (95% CI, 25.7–33.5) with functional limitations; 45-y-old men can expect to live 41% (95% CI, 35%–46%) of their remaining 25.4 y (95% CI, 23.3–28.8) with such limitations. Disabilities related to functional limitations are shown to have a substantial negative effect on individuals' labor activities, and are negatively related to subjective well-being.
Conclusions
Individuals in this population experience a lengthy struggle with disabling conditions in adulthood, with high probabilities of remitting and relapsing between states of functional limitation. Given the strong association of disabilities with work efforts and subjective well-being, this research suggests that current national health policies and international donor-funded health programs in SSA inadequately target the physical health of mature and older adults.
Please see later in the article for the Editors' Summary
Editors' Summary
Background
The population of the world is getting older. In almost every country, the over-60 age group is growing faster than any other age group. In 2000, globally, there were about 605 million people aged 60 years or more; by 2050, 2 billion people will be in this age group. Much of this increase in the elderly population will be in low-income countries. In sub-Saharan Africa, for example, 10% of the population is currently aged 45 years or more, but by 2060, a quarter of the population will be so-called mature adults. In all countries, population aging is the result of women having fewer children (falling fertility) and people living longer (increasing life expectancy). Thus, population aging is a demographic transition, a change in birth and death rates. In low- and middle-income countries, population aging is occurring in parallel with an “epidemiological transition,” a shift from communicable (infectious) diseases to non-communicable diseases (for example, heart disease) as the primary causes of illness and death.
Why Was This Study Done?
Both the demographic and the epidemiological transition have public health implications for low-income countries. Good health is important for the independence and economic productivity of older people. Productive older people can help younger populations financially and physically, and help compensate for the limitations experienced by younger populations infected with HIV. Also, low-income countries lack social safety nets, so disabled older adults can be a burden on younger populations. Thus, the health of older individuals is important to the well-being of people of all ages. As populations age, low-income countries will need to invest in health care for mature and elderly adults and in disease prevention programs to prevent or delay the onset of non-communicable diseases, which can limit normal daily activities by causing disabilities. Before providing these services, national policy makers need to know the proportion of their population with disabilities, the functional limitations caused by poor physical health, and the health expectancies (the number of years a person can expect to be in good health) of older people in their country. In this cohort modeling study, the researchers estimate health expectancies and transition rates between different levels of disability among mature adults in Malawi, one of the world's poorest countries, using data collected by the Malawi Longitudinal Survey of Families and Health (MLSFH) on economic, social, and health conditions in a rural population. Because Malawi has shorter life expectancies and earlier onset of disability than wealthier countries, the authors considered individuals aged 45 and older as mature adults at risk for disability.
What Did the Researchers Do and Find?
The researchers categorized the participants in the 2006, 2008, and 2010 waves of the MLSFH into three levels of functional limitation (healthy, moderately limited, and severely limited) based on answers to questions in the SF-12 health survey questionnaire that ask about disabilities that limit daily activities that rural Malawians perform. The researchers estimated age–gender patterns of functional limitations and transition rates between different disability states using a discrete-time hazard model, and health expectancies by running a microsimulation to model the aging of synthetic cohorts with various starting ages but the same gender and functional limitation distributions as the study population. These analyses show that the chance of becoming physically disabled rises sharply with age, with 45-year-old women in rural Malawi expected to spend 58% of their estimated remaining 28 years with functional limitations, and 45-year-old men expected to live 41% of their remaining 25.4 years with functional limitations. Also, on average, a 45-year-old woman will spend 2.7 years with moderate functional limitation and 0.6 years with severe functional limitation before she reaches 55; for men the corresponding values are 1.6 and 0.4 years. Around 50% of moderately and 60%–80% of severely limited individuals stated that pain interfered quite a bit or extremely with their normal work during the past four weeks, suggesting that pain treatment may help reduce disability.
What Do These Findings Mean?
These findings suggest that mature adults in rural Malawi will have some degree of disability during much of their remaining lifetime. The risks of experiencing functional limitations are higher and the onset of persistent disabilities happens earlier in Malawi than in more developed contexts—the proportions of remaining life spent with severe limitations at age 45 in Malawi are comparable to those of 80-year-olds in the US. The accuracy of these findings is likely to be affected by assumptions made during modeling and by the quality of the data fed into the models. Nevertheless, these findings suggest that functional limitations, which have a negative effect on the labor activity of individuals, will become more prominent in Malawi (and probably other sub-Saharan countries) as the age composition of populations shifts over the coming years. Older populations in sub-Saharan Africa are not targeted well by health policies and programs at present. Consequently, these findings suggest that policy makers will need to ensure that additional financial resources are provided to improve health-care provision for aging individuals and to lessen the high rates of functional limitation and associated disabilities.
Additional Information
Please access these websites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.1001435.
This study is further discussed in a PLoS Medicine Perspective by Andreas Stuck, et al.
The World Health Organization provides information on many aspects of aging (in several languages); the WHO Study on Global Ageing and Adult Health (SAGE) is compiling longitudinal information on the health and well-being of adult populations and the aging process
The United Nations Population Fund and HelpAge International publication Ageing in the Twenty-First Century is available
HelpAge International is an international nongovernmental organization that helps older people claim their rights, challenge discrimination, and overcome poverty, so that they can lead dignified, secure, and healthy lives
More information on the Malawi Longitudinal Study of Families and Health is available
doi:10.1371/journal.pmed.1001435
PMCID: PMC3646719  PMID: 23667343
24.  Averting Obesity and Type 2 Diabetes in India through Sugar-Sweetened Beverage Taxation: An Economic-Epidemiologic Modeling Study 
PLoS Medicine  2014;11(1):e1001582.
In this modeling study, Sanjay Basu and colleagues estimate the potential health effects of a sugar-sweetened beverage taxation among various sub-populations in India over the period 2014 to 2023.
Please see later in the article for the Editors' Summary
Background
Taxing sugar-sweetened beverages (SSBs) has been proposed in high-income countries to reduce obesity and type 2 diabetes. We sought to estimate the potential health effects of such a fiscal strategy in the middle-income country of India, where there is heterogeneity in SSB consumption, patterns of substitution between SSBs and other beverages after tax increases, and vast differences in chronic disease risk within the population.
Methods and Findings
Using consumption and price variations data from a nationally representative survey of 100,855 Indian households, we first calculated how changes in SSB price alter per capita consumption of SSBs and substitution with other beverages. We then incorporated SSB sales trends, body mass index (BMI), and diabetes incidence data stratified by age, sex, income, and urban/rural residence into a validated microsimulation of caloric consumption, glycemic load, overweight/obesity prevalence, and type 2 diabetes incidence among Indian subpopulations facing a 20% SSB excise tax. The 20% SSB tax was anticipated to reduce overweight and obesity prevalence by 3.0% (95% CI 1.6%–5.9%) and type 2 diabetes incidence by 1.6% (95% CI 1.2%–1.9%) among various Indian subpopulations over the period 2014–2023, if SSB consumption continued to increase linearly in accordance with secular trends. However, acceleration in SSB consumption trends consistent with industry marketing models would be expected to increase the impact efficacy of taxation, averting 4.2% of prevalent overweight/obesity (95% CI 2.5–10.0%) and 2.5% (95% CI 1.0–2.8%) of incident type 2 diabetes from 2014–2023. Given current consumption and BMI distributions, our results suggest the largest relative effect would be expected among young rural men, refuting our a priori hypothesis that urban populations would be isolated beneficiaries of SSB taxation. Key limitations of this estimation approach include the assumption that consumer expenditure behavior from prior years, captured in price elasticities, will reflect future behavior among consumers, and potential underreporting of consumption in dietary recall data used to inform our calculations.
Conclusion
Sustained SSB taxation at a high tax rate could mitigate rising obesity and type 2 diabetes in India among both urban and rural subpopulations.
Please see later in the article for the Editors' Summary
Editors' Summary
Background
Non-communicable diseases (NCDs) and obesity (excessive body mass) are major threats to global health. Each year NCDs kill 36 million people (including 29 million people in low- and middle-income countries), thereby accounting for nearly two-thirds of the world's annual deaths. Cardiovascular diseases, cancers, respiratory diseases, and diabetes (a condition characterized by raised blood sugar levels) are responsible for most NCD-related deaths. Worldwide, diabetes alone affects about 360 million people and causes nearly 5 million deaths annually. And the number of people affected by NCDs is likely to rise over the next few decades. It is estimated, for example, that 101.2 million people in India will have diabetes by 2030, nearly double the current number. In Asia and other low- and middle-income countries overweight as well as obesity represent a risk factor for NCDs and the global prevalence of obesity (the proportion of the world's population that is obese) has nearly doubled since 1980. Worldwide, around 0.5 billion people are now classified as obese and about 1.5 billion more overweight. That is, they have a body mass index (BMI) of 30 kg/m2 or more (25–30 for overweight); BMI is calculated by dividing a person's weight in kilograms by the square of their height in meters. In India individuals with a BMI of 25 or more (overweight/obese) are at very high risk of diabetes.
Why Was This Study Done?
The consumption of sugar-sweetened beverages (SSBs, soft drinks sweetened with cane sugar or other caloric sweeteners) is a major risk factor for overweight/obesity and, independent of total energy consumption and BMI, for type 2 diabetes (the commonest form of diabetes). In high-income countries, SSB taxation has been proposed as a way to lower the risk of obesity and type 2 diabetes, however it is unknown if this approach will work in low- and middle-income countries. Here, in an economic-epidemiologic modeling study, researchers estimate the potential health effects of SSB taxation in India, a middle-income country in which total SSB consumption is rapidly increasing, but where SSB consumption and chronic disease risk vary greatly within the population and where people are likely to turn to other sugar-rich beverages (for example, fresh fruit juices) if SSBs are taxed.
What Did the Researchers Do and Find?
The researchers used survey data relating SSB consumption to price variations to calculate how changes in the price of SSBs affect the demand for SSBs (own-price elasticity) and for other beverages (cross-price elasticity) in India. They combined these elasticities and data on SSB sales trends, BMIs, and diabetes incidence (the frequency of new diabetes cases) into a mathematical microsimulation model to estimate the effect of a 20% tax on SSBs on caloric (energy) consumption, glycemic load (an estimate of how much a food or drink raises blood sugar levels after consumption; low glycemic load diets lower diabetes risk), the prevalence of overweight/obesity, and the incidence of diabetes among Indian subpopulations. According to the model, if SSB sales continue to increase at the current rate, compared to no tax, a 20% SSB tax would reduce overweight/obesity across India by 3.0% and the incidence of type 2 diabetes by 1.6% over the period 2014–2023. In absolute figures, a 20% SSB tax would avert 11.2 million cases of overweight/obesity and 400,000 cases of type 2 diabetes between 2014 and 2023. Notably, if SSB sales increase more steeply as predicted by drinks industry marketing models, the tax would avert 15.8 million cases of overweight/obesity and 600,000 cases of diabetes. Finally, the model predicted that the largest relative effect of an SSB tax would be among young men in rural areas.
What Do These Findings Mean?
The accuracy of these findings is likely to be affected by the assumptions incorporated in the model and by the data fed into it. In particular, the accuracy of the estimates of the health effects of a 20% tax on SSBs is limited by the assumption that future consumer behavior will reflect historic behavior and by potential underreporting of SSB consumption in surveys. Nevertheless, these findings suggest that a sustained high rate of tax on SSBs could mitigate the rising prevalence of obesity and the rising incidence of diabetes in India in both urban and rural populations by affecting both caloric intake and glycemic load. Thus, SSB taxation might be a way to control obesity and diabetes in India and other low- and middle-income countries where, to date, large-scale interventions designed to address these threats to global health have had no sustained effects.
Additional Information
Please access these websites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.1001582.
The World Health Organization provides information about non-communicable diseases, obesity, and diabetes around the world (in several languages)
The US Centers for Disease Control and Prevention provides information on non-communicable diseases around the world and on overweight and obesity and diabetes (including some information in Spanish)
The US National Diabetes Information Clearinghouse provides information about diabetes for patients, health-care professionals, and the general public, including detailed information on weight control (in English and Spanish)
The UK National Health Service Choices website provides information for patients and carers about type 2 diabetes and about obesity; it includes personal stories about diabetes and about obesity
MedlinePlus provides links to further resources and advice about diabetes and diabetes prevention and about obesity (in English and Spanish)
A 2012 Policy brief from the Yale Rudd Center for food policy and obesity provides information about SSB taxes
doi:10.1371/journal.pmed.1001582
PMCID: PMC3883641  PMID: 24409102
25.  Maternal Overweight and Obesity and Risks of Severe Birth-Asphyxia-Related Complications in Term Infants: A Population-Based Cohort Study in Sweden 
PLoS Medicine  2014;11(5):e1001648.
Martina Persson and colleagues use a Swedish national database to investigate the association between maternal body mass index in early pregnancy and severe asphyxia-related outcomes in infants delivered at term.
Please see later in the article for the Editors' Summary
Background
Maternal overweight and obesity increase risks of pregnancy and delivery complications and neonatal mortality, but the mechanisms are unclear. The objective of the study was to investigate associations between maternal body mass index (BMI) in early pregnancy and severe asphyxia-related outcomes in infants delivered at term (≥37 weeks).
Methods and Findings
A nation-wide Swedish cohort study based on data from the Medical Birth Register included all live singleton term births in Sweden between 1992 and 2010. Logistic regression analyses were used to obtain odds ratios (ORs) with 95% CIs for Apgar scores between 0 and 3 at 5 and 10 minutes, meconium aspiration syndrome, and neonatal seizures, adjusted for maternal height, maternal age, parity, mother's smoking habits, education, country of birth, and year of infant birth. Among 1,764,403 term births, 86% had data on early pregnancy BMI and Apgar scores. There were 1,380 infants who had Apgar score 0–3 at 5 minutes (absolute risk  = 0.8 per 1,000) and 894 had Apgar score 0–3 at 10 minutes (absolute risk  = 0.5 per 1,000). Compared with infants of mothers with normal BMI (18.5–24.9), the adjusted ORs (95% CI) for Apgar scores 0–3 at 10 minutes were as follows: BMI 25–29.9: 1.32 (1.10–1.58); BMI 30–34.9: 1.57 (1.20–2.07); BMI 35–39.9: 1.80 (1.15–2.82); and BMI ≥40: 3.41 (1.91–6.09). The ORs for Apgar scores 0–3 at 5 minutes, meconium aspiration, and neonatal seizures increased similarly with maternal BMI. A study limitation was lack of data on effects of obstetric interventions and neonatal resuscitation efforts.
Conclusion
Risks of severe asphyxia-related outcomes in term infants increase with maternal overweight and obesity. Given the high prevalence of the exposure and the severity of the outcomes studied, the results are of potential public health relevance and should be confirmed in other populations. Prevention of overweight and obesity in women of reproductive age is important to improve perinatal health.
Please see later in the article for the Editors' Summary
Editors' Summary
Background
Economic, technologic, and lifestyle changes over the past 30 years have created an abundance of cheap, accessible, high-calorie food. Combined with fewer demands for physical activity, this situation has lead to increasing body mass throughout most of the world. Consequently, being overweight or obese is much more common in many high-income and low-and middle-income countries compared to 1980. Worldwide estimates put the percentage of overweight or obese adults as increasing by over 10%, between 1980 and 2008.
As being overweight becomes a global epidemic, its prevalence in women of reproductive age has also increased. Pregnant women who are overweight or obese are a cause for concern because of the possible associated health risks to both the infant and mother. Research is necessary to more clearly define these risks.
Why Was This Study Done?
In this study, the researchers investigated the complications associated with excess maternal weight that could hinder an infant from obtaining enough oxygen during delivery (neonatal asphyxia). All fetuses experience a loss of oxygen during contractions, however, a prolonged loss of oxygen can impact an infant's long-term development. To explore this risk, the researchers relied on a universal scoring system known as the Apgar score. An Apgar score is routinely recorded at one, five, and ten minutes after birth and is calculated from an assessment of heart rate, respiratory effort, and color, along with reflexes and muscle tone. An oxygen deficit during delivery will have an impact on the score. A normal score is in the range of 7–10. Body mass index (BMI) a calculation that uses height and weight, was used to assess the weight status (i.e., normal, overweight, obese) of the mother during pregnancy.
What Did the Researchers Do and Find?
Using the Swedish medical birth registry (a database including nearly all the births occurring in Sweden since 1973) the researchers selected records for single births that took place between 1992 to 2010. The registry also incorporates prenatal care data and researchers further selected for records that included weight and height measurement taken during the first prenatal visit. BMI was calculated using the weight and height measurement. Based on BMI ranges that define weight groups as normal, overweight, and obesity grades I, II, and III, the researchers analyzed and compared the number of low Apgar scoring infants (Apgar 0–3) in each group. Mothers with normal weight gave birth to the majority of infants with Apgar 0–3. In comparison the proportion of low Apgar scores were greater in babies of overweight and obese mothers. The researchers found that the rates of low Apgar scores increased with maternal BMI: the authors found that rates of low Apgar score at 5 minutes increased from 0.4 per 1,000 among infants of underweight women (BMI <18.5) to 2.4 per 1,000 among infants of women with obesity class III (BMI ≥40). Furthermore, overweight (BMI 25.0–29.9) was associated with a 55% increased risk of low Apgar scores at 5 minutes; obesity grade I (BMI 30–34.9) and grade II (BMI 35.0–39.9) with an almost 2-fold and a more than 2-fold increased risk, respectively; and obesity grade ΙΙΙ (BMI ≥40.0) with a more than 3-fold increase in risk. Finally, maternal overweight and obesity also increase the risks for seizures and meconium aspiration in the neonate.
What Do These Findings Mean?
These findings suggest that the risk of experiencing an oxygen deficit increases for the babies of women who are overweight or obese. Given the high prevalence of overweight and obesity in many countries worldwide, these findings are important and suggest that preventing women of reproductive age from becoming overweight or obese is therefore important to the health of their children.
A limitation of this study is the lack of data on the effects of clinical interventions and neonatal resuscitation efforts that may have been performed at the time of birth. Also Apgar scoring is based on five variables and a low score is not the most direct way to determine if the infant has experienced an oxygen deficit. However, these findings suggest that early detection of perinatal asphyxia is particularly relevant among infants of overweight and obese women although more studies are necessary to confirm the results in other populations.
Additional Information
Please access these Web sites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.1001648.
The US National Institutes of Health explains and calculates body mass index
The NIH also defines the Apgar scoring system
The United Kingdom's National Health Service has information for pregnant woman who are overweight
The UK-based Overseas Development Institute discusses how changes in diet have led to a worldwide health crisis in its “Future Diets” publication
Information about the Swedish health care system is available
Information in English is available from the National Board of Health and Welfare in Sweden
doi:10.1371/journal.pmed.1001648
PMCID: PMC4028185  PMID: 24845218

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